SlideShare ist ein Scribd-Unternehmen logo
1 von 74
Downloaden Sie, um offline zu lesen
Healthcare Research @ AI Institute:
dHealth, public health, epidemiology, biomedicine
Overview Presentation to the MUSC’s AI Hub and others
May 2021
Amit Sheth, Founding Director http://aiisc.ai
© 2021 NAVER. All rights reserved.
“
3
AIISC in core AI areas, and
interdisciplinary AI/AI applications
>> 25 researchers
including 4 faculty
(6 in Fall 2021), 2-3
postdocs, ~20 PhD
students, >10
MS/BS and several
interns/associates
© 2021 NAVER. All rights reserved.
D-Health, health informatics, public health, epidemiology –
sample collaborations (Pending & PLANNED Submissions only)
•College of Medicine/Prisma/Prisma-Upstate
•Mental health [M Natarajan], Addiction [A. Litwin], Asthma [R. Dawson], Diabetes & Obesity
[L. Knight], Hypertension - Diet & Nutrition [S. Donevant], Neutropenia [S. Craemer]
•College of Pharmacy: EOCRC [P. Backhaults, L. Hofseth, et al]
•College of Nursing: COVID-19 mobile app [R. Hughes, S. Donevant], mental health
chatbot [R. Hughes, S. Donevant, P. Raynor]
•Arnold School of Public Health: Mental Health [S. Qiao], Healthcare Big Data -
Education & Training [X. Li, et. al.]
•USCAND, Inst of Mind & Brain: Neuroscience [R. Desai] and neurodevelopmental
diseases [J. Bradshaw, J. Roberts]
•CEC- IIT, BME, CSE: Health IT/Smart Health [E. Regan], UI/UX [D. Wu]; Health
mApp [N. Boltin], (several in CSE).
Projects
➢ KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care (NICHD)
➢ mHealth to Improve Carbohydrate Counting Accuracy in Pediatric Type 1 Diabetes
➢ Improving mental health of COVID-19 patients with an Artificial Intelligence-based chatbot
➢ Personalized Virtual Health Assistant Enabled by Knowledge-infused Reinforcement Learning for Adaptive
Mental Health Self-management
➢ Characterizing and supporting help seekers on social media using expert-in-the-loop learning
➢ Modeling Social Behavior for Healthcare Utilization in Depression (NIMH)
➢ eDarkTrends : Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use
(NIDA)
➢ Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use (NIDA)
➢ Innovative NIDA National Early Warning System Network (iN3) (NIDA)
➢ PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology (NIDA)
➢ Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest (NSF)
➢ Discrepancies in Diagnosis and Treatment of Cardiovascular Disease Based on Sex and Gender to Improve
Women’s Health (NHLBI)
➢ Early Onset of Colorectal Cancer
➢ Digestive Inflation Index
➢ more ...
Types of Healthcare Data
◎ EMR
◎ Social Media (Reddit,
Twitter,Web Forums)
◎ Conversations: Patient-
Clinician, Virtual Health
Assistant-Patient
◎ Patient Generated:
Wearable/sensor data,
mApp data
◎ Images: food, fMRI
AI Techniques and Technologies
◎ Knowledge Graphs/Ontologies
(contextualization, personalization,
abstraction)
◎ NLP/NLU
◎ Machine Learning/Deep Learning
(RL, GAN, CNN, LSTM,....)
◎ Conversational AI, Q/A
◎ mApp, Virtual Health Assistants
(Chatbots)
◎ Health sensors/IoTs/mobile
devices
Medical Conditions/Healthcare Challenges addressed
◎ Asthma
◎ Mental Health
◎ Addiction
◎ COVID-19
◎ Cardiovascular Disease
◎ Type 1 Diabetes in Children
◎ Adult Diabetes - Hypertension
◎ Neutropenia
◎ Sleep Disorders
◎ Gender and Race Disparity
◎ Demographics
◎ SDOH
◎ Drug Design
Partners:
Weill Cornell, UCSF Medical,
Prisma-Health, Addiction
Research Center, UofSC
Medical/Pharma/Public
Health/Nursing; Wright State
Physicians, ….
Unique Value Propositions and Strengths [for Health Apps]
◎ Development of Knowledge Graphs
◎ Knowledge-infused (Deep) Learning and Knowledge-
infused NLP: Explainable AI
◎ Conversational AI/collaborative agents
◎ Augmented Personalized Health
10
Health Knowledge Graph
Drug Abuse Ontology
Lokala U, Daniulaityte R, Lamy F, Gaur M, Thirunarayan K, Kursuncu U, Sheth. A. (2020).
DAO: An ontology for substance use epidemiology on social media and dark web. JMIR.
https://doi.org/10.2196/preprints.24938
https://scholarcommons.sc.edu/aii_fac_pub/356/ [Shah and Sheth US patent 2015]
Knowledge-Infused
Learning with
Semantic,
Cognitive,
Perceptual
Computing
Framework
11
Overarching Theory
Knowledge
Domain (Ontology)
Personalized KG
Multisensory
Sensing &
Multimodal
Data Interactions
Images
Text Speech Videos
IoTs
Natural Language
Processing,
Machine with
Deep Learning
AUGMENTED PERSONALIZED
HEALTH (APH)
Modeling broader disease context, and
personalized user behavior
Reasoning & decision-
making framework
Minimize data overload, assist in making
choices, appraisal, recommendations
TEDx talk: Augmented Health with Personalized Data and AI
12
Contextualization and
Personalization
kBOT initiates greeting
conversation.
Understands the patient’s health
condition (allergic reaction to high
ragweed pollen level) via the
personalized patient’s knowledge
graph generated from EMR,
PGHD, and prior interactions with
the kBot.
Generates predictions or
recommended course of actions.
Inference based on patient’s
historical records and
background health knowledge
graph containing contextualized
(domain-specific) knowledge.
Figure: Example kBot conversation which
utilizes background health knowledge graph
and patient’s knowledge graph to infer and
generate recommendation to patients.
★ Conversing only information relevant
to the patient
Context enabled by relevant
healthcare knowledge including
clinical protocols.
Why Knowledge
Infused Learning
(K-IL)?
By changing the inputs, it can
enrich the representation (E.g.
Radicalization on Social Media)
By changing parameters, we can
control the learned
patterns/correlations to adhere to
the knowledge.
Deep Infusion would allow us finer
grained control over learned
patterns to ensure adherence to
knowledge at every step of the
hierarchy
Explanations easy to derive from
the KG used 13
Contextual Modeling to
analyze Radicalization on
Social Media
(Hate)
Health-e Gamecock COVID-19 daily status check (on Apple Store)
Health-e Gamecock
15
What? Comprehensive, Customizable, Adaptive
● App/chatbot for individuals
○ Cohort 1: College of Nursing (testing, research)
○ Cohort 2: Students, Staff, Faculty
● Dashboards for different uses: general administration, health services, research
○ Support for custom protocol
Gamecock look-n-feel, easy to use and engaging, customizable, secure, privacy
disclosure/management, HIPAA compliance, accessibility (ADA compliance), scalable, well
tested, extensible
Why? Proactively keep Campus healthy
● Informing and Educating the Community, Regularly Check Health Status:
○ Stay informed and educated, make better individual decisions, feel safer, reduce
adverse outcomes
○ In case of concern, connect with Student Health Services, manage isolation protocols
● Providing Campus-wide View: tools needed to make campus-wide decisions
○ Insurance against possible adverse outcome from low-risk approach (CDC’s
Considerations for Institutes of Higher Education (May 21), proactive
implementation of protocols
● Research: COVID-19, Mental Health
How?
● Comprehensive campus-wide involvement and coordination: requirements,
development, testing, operations
○ College of Nursing: lead evaluation
○ Student Health Services
○ School of Public Health
○ Division of Information Technology
○ CEC; of course, the AI Institute
● Development team with extensive experience
● Much more functional, forward looking, and cheaper than vendors
AI & Healthcare @ AIISC: May 2021 Snapshot
Demo: Health-e Gamecock COVID-19 App (WebApp, IOS, Android)
Note: development is now complete, app is evaluated and we plan to use it for a major study.
Health-e Gamecock COVID-19 daily status check (on Apple Store)
Augmented Personalized Health
Check out the TEDx talk and the original article
22
AI & Healthcare @ AIISC: May 2021 Snapshot
24
Use Case: kHealth Asthma
Many Sources of Highly Diverse Data
(& collection methods: Active + Passive):
Up to 1852 data points/ patient /day
http://bit.ly/kHealth-Asthma
kBot with screen
interface for conversation
Images
Text
Speech
★ Episodic to Continuous Monitoring
★ Clinician-centric to Patient-centric
★ Clinician controlled to Patient-empowered
★ Disease Focused to Wellness-focused
★ Sparse data to Multimodal Big Data
*(Asthma-Obesity)
Self Monitoring with kHealthDash:
Knowledge enabled personalized DASHboard for Asthma Management
Video link - https://youtu.be/yUgXCPwc55M
Digital Phenotype Score vs Asthma Control Test Score
Digital Phenotype Score = Symptom Score + Rescue Score + Activity Score + Awakening Score
27
Utkarshani Jaimini, Krishnaprasad
Thirunarayan, Maninder Kalra, Revathy
Venkataramanan, Dipesh Kadariya, Amit
Sheth, “How Is My Child’s Asthma?”
Digital Phenotype and Actionable Insights
for Pediatric Asthma”, JMIR Pediatr Parent
2018;1(2):e11988, DOI: 10.2196/11988.
Self Appraisal with Digital Phenotype Score
Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma, JMIR Pediatric Parent 2018;1(2):e11988
https://medium.com/leoilab/digital-phenotyping-turning-our-smartphones-inward-141a75b2f2a3
● Digital Phenotype Score (DPS) is defined as the score
to quantify the digital phenotypes collected from the
social media, smartphones, wearables, and sensors
streams.
● DPS acts as a cumulative measure for the abstraction
of knowledge and information from the raw digital
phenotypic data.
● The integration of the DPS can enable personalized
interventions in real time which are directly responsive
to the healthcare need of a patient.
Using Knowledge Graphs to construct a contextualized and personalized profile for each patient
that can drive insights and personalized care strategies
● Published in ISWC 2018 Contextualized Knowledge Graph Workshop, 2018. Amelie
Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth
2018, ISWC.
● Sheth, A., Jaimini, U., & Yip, H. Y. (2018). How Will the Internet of Things Enable Augmented
Personalized Health? IEEE Intelligent Systems, 33(1), 89–97.
https://doi.org/10.1109/MIS.2018.012001556
Determining Personalized Asthma Triggers: Seasonal Dependency
Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300
PMID: 31518318 PMCID: 6716491
© 2021 NAVER. All rights reserved.
Evidence based Path to Personalization
Patient-A was monitored for 13 weeks encompassing winter to spring 2018. Type: Severe, low medication compliance.
Absence of Pollen
First 6 weeks
Presence of Pollen
Rest of the 7 weeks
Pre (observe)
4 weeks
Post (validate)
2 weeks
Pre
4 weeks
Post
3 weeks
Pollen 0 Pollen 0 days Pollen 17 days Pollen 3 days
PM2.5 20 days PM2.5 5 days PM2.5 14 days PM2.5 2 days
Ozone 1 day Ozone 0 Ozone 0 Ozone 1 day
Asthma
Episodes*
21 days Asthma
Episodes
5 days Asthma
Episodes
17 days Asthma
Episodes
3 days
● Absence of Pollen - PM2.5 is the trigger
● Presence of Pollen - Pollen and PM2.5. Severe symptoms occurred in this period. Presence of both PM2.5 and
Pollen increased the intensity of asthma episodes.
Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300
PMID: 31518318 PMCID: 6716491
Interventional Strategy
Actionable insights using Digital Phenotype Score and Controller
Compliance Score
Clinical Interviews
35
36
Q1: Do you feel restless in sleep?
A1: Two-three times a week
Q2: Do you wake early? How often does it happen?
A2: Too early, happens two times a week.
Q3: Do you persistently feel sad?
A3: Yes. A sense of loneliness
Q4: How often you stay alone?
A4: I am divorced.
Q5: Have you been diagnosed with PTSD?
A5: No
Q6: Have you seen an MHP for your anxiety disorder?
A6: hmm recently.
Participant was asked do they feel restless in sleep,
then participant said hmm,two three times week
Participant was asked, do they persistently feel sad,
participant said divorce loneliness
Participant was asked, have they been diagnosed with
PTSD, participant said hmm recently.
Diagnostic Interview Summaries
Contextualized Diagnosis of
patient conversations
Correct diagnosis: Post Traumatic Stress
Disorder
AI & Healthcare @ AIISC: May 2021 Snapshot
AI & Healthcare @ AIISC: May 2021 Snapshot
AI & Healthcare @ AIISC: May 2021 Snapshot
AI & Healthcare @ AIISC: May 2021 Snapshot
Online Mental Health Support
Social Media: Reddit
41
Interest
We are interested in
Matching Support Seekers
-SSs (left) with Support
Providers - SPs (right)
Current State
Currently, moderators
(center) do this matching
42
Proposal
Our AI system will replace/assist the moderators that use
medical knowledge, information about the user, extracted
from the posts to perform this matching
43
Example of matching on Reddit
Using knowledge and lexicons with deep learning for matching users on Reddit
AI & Healthcare @ AIISC: May 2021 Snapshot
Addiction (Opioid, Cannabis,
Synthetic Cannabinoid,
Prescription Drug Abuse) X
Epidemiology
46
AI & Healthcare @ AIISC: May 2021 Snapshot
Knowledge Graph for better Information Extraction: Application
in Epidemiology
48
Cameron, Delroy, Gary A. Smith, Raminta Daniulaityte, Amit P. Sheth, Drashti Dave, Lu Chen, Gaurish Anand, Robert Carlson, Kera Z. Watkins, and Russel Falck.
"PREDOSE: a semantic web platform for drug abuse epidemiology using social media." Journal of biomedical informatics 46, no. 6 (2013): 985-997.
Knowledge-infused via DAO Ontology for solving relation
between Cannabis and Depression
K-IL: Shallow Infusion with DAO in DL model to detect trends in
cryptomarkets
Table: Sample properties derived from cryptomarket with DAO
Motivation
● The opioid epidemic entrenched in
Ohio and the Midwest of the US.
● The prevalence of opioid and its
impact on the well-being of
individuals and the society in Ohio.
○ Mental Health & Suicide Risk
Questions
1. How can we use social media to measure
mental health impact of opioid
prevalence?
1. Are there association between opioid and
mental health/suicide risk based on social
media data?
Approach
Monitoring the prevalence of opioid and its impact on mental health and suicide in Ohio,
utilizing a scalable knowledge and data driven BIGDATA (BD) approach via social media.
BD Spoke: Opioid and Substance Use in Ohio
Score
Calculation
Opioid
Mental Health
Depression
Addiction
Suicide Risk
Ideation, Behavior
Attempt
Correlations
● Sheth, Amit, and Pavan Kapanipathi. "Semantic filtering for social data." IEEE Internet Computing 20, no. 4 (2016): 74-78.
● Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, and Amit Sheth. 2018. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical
Language Models. In Proceedings of the 27th International Conference on Computational Linguistics (COLING2018), pages 1986-1997. Association for Computational Linguistics
● Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018, October). " Let Me Tell You About Your Mental Health!" Contextualized
Classification of Reddit Posts to DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 753-
762).
● Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., ... & Pathak, J. (2019, May). Knowledge-aware assessment of severity of suicide risk for early
intervention. In The World Wide Web Conference (pp. 514-525).
● Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., ... & Sheth, A. (2017, July). Semi-supervised approach to monitoring clinical depressive
symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 1191-1198).
● Daniulaityte, R., Nahhas, R. W., Wijeratne, S., Carlson, R. G., Lamy, F. R., Martins, S. S., ... & Sheth, A. (2015). “Time for dabs”: Analyzing Twitter data on marijuana concentrates across
News
Articles
Twitter
Data
Domain
Knowledg
e
Content
Enrichment
DAO
DSM-5
Location Extraction
Keyphrase Extraction
Age-based
Clustering
Semantic Filtering
Entity
Extraction
NLM Training
f(.)
Knowledge Infused
Natural Language
Processing (Ki-NLP)
Semantic
Mapping
Semantic
Proximity
Topic Model
Language Model
DAO
DSM-5
Dashboard
Visualizations
(Online)
Offline
Analysis
&
Visualizations
BD Spoke: Opioid and Substance Use in Ohio
● Substance use addictive disorder linked to
opioid with higher correlation.
● Gender dysphoria, Dissociative and OCD
disorders are correlating moderately.
Opioid Prevalence in Ohio vs. Mental Health & Suicide
● Suicide ideation (initial stage) with highest correlation.
● Mild severity level of suicide risk linked to higher
correlation.
● Weak correlation for suicide indication (before initial)
p
N counties
p
N counties
with a Social Quality Index
Insights from semantic analysis of Social Media Big Data
Psychidemic: Measuring the Spatio-Temporal
Psychological Impact of Novel Coronavirus
with a Social Quality Index
Insights from semantic analysis of Social Media Big Data
○ Mental Health: Depression, Anxiety
○ Addiction: Substance use/abuse
○ Gender-based/Domestic Violence
○ Mental Health: Depression, Anxiety
○ Addiction: Substance use/abuse
○ Gender-based/Domestic Violence
Capability Demonstration 2
● Spatio-temporal analysis of big data (1billion tweets,
700,000 articles)
● Use of domain knowledge graphs (mental health,
addiction)
● Complex language understanding (slang, specialized
domain terms)
○ Can do people/demographic, network, sentiment, emotion, intent
analysis
● Scenario: Understand the implication of policy
choices (e.g., school/business closing) and real-
world events during COVID-19
55
A calculated Social Quality Index (SQI)
aggregates mental health components
(Depression, Anxiety), Addiction and
Substance Use Disorders.
Social Quality Index (SQI)
vecteezy.com
● Change in SQI informs comparisons between
states.
● Raw transformed SQI into relative state rankings
changing over time.
e.g., IN, NH, OH,
OR, WA, WY
are worsening.
Results: Relative State Rankings Reveal Patterns
SQI Ranking April 4 - 10
SQI Ranking March 14 - 20 SQI Ranking March 21 - 27
SQI Ranking March 23-April 3
Darker: Better
Social Quality
Results: Cluster --Improving SQI Ranking
SQI bad SQI better SQI better
SQI better
Frequency
Depression: 125037
Addiction: 92897
Anxiety: 81891
Total: 299825
Frequency
Depression: 113830
Addiction: 81810
Anxiety: 74080
Total: 269720
Frequency
Depression: 81463
Addiction: 60166
Anxiety: 45998
Total: 187627
Frequency
Depression: 59088
Addiction: 49086
Anxiety: 46887
Total: 155061
IL, NY, MD,
AZ, NM, MA.
March 14-20 March 21-27 March 28-April 3
April 4-10
External Events
● Stay at home order
● Extension non-essential closure
● Closing parks
● State of emergency
● School Closure
● Mental Health Alarm
● Extension of small business closure
● Bill payment deadline extended
● 41k new job openings
● Child-care assistance for essential
workers
● Spike in number of
cases
● Stay at home order
extended
● Extension School
Closure
● State of emergency
Extension
● Unemployment
Increase(>800%)
● Tax deadline extended
● SNAP Benefits
● Death Benefits
● Domestic Violence Alarm
● Spike in number of cases
● Stay at home order extended
● Extension School Closure
● State of emergency Extension
● Closure barber shops and related
businesses
● Number of deaths cross 50000
● Unemployment Increase(>2.5k%)
● Tax deadline extended
● Phases reopening
● Limited indoor seating or gathering
● CARES Act
Change
in
SQI
relative
to
first
week
AI & Healthcare @ AIISC: May 2021 Snapshot
Mental Health Dialogue
61
AI & Healthcare @ AIISC: May 2021 Snapshot
Construction of Personalized Knowledge Graph:
Knowledge-infused Reinforcement Learning
● The input to the agent is sequential through many steps, it gets an input and a reward at every step and
learns the right output gradually through reinforcement.
APH Self Management: Reinforcement Learning
NOURICH
A system to monitor diet,
recommend meals and promote
healthy eating habits:
Current application: Type 1 diabetes
in Children
Contact: Revathy Venkataramanan
Acknowledgement: Thanks to my collaborators Hong Yung Yip and Thilini Wijayasriwartane for the slides
NOURICH
Know What you Eat
Overweight Obesity Hospitalization
Prevent
overweight
moving to
obesity
Prevent obesity
leading to
hospitalization
Bridging the gap
“Focusing on reducing excess and impulsive calorie
consumption and making an informed decision about food
choices and physical activity can help one attain a healthier
weight and minimize the risk of chronic illness”
The Dietary Guidelines of Americans 2010
● Real-time food recognition
● Tensorflow Mobile net model
20
Food Categories
700
Images/Category
Image Data Source
Image Source: https://commons.wikimedia.org/wiki/File:Google_%22G%22_Logo.svg, https://freepik.com
Nutrition Management System
NOURICH
GOAL: A system to monitor
diet, recommend meals and
promote healthy eating habits.
Architecture for application to Type 1 Diabetes in Children
Data sources
- User specific (food allergies,
comorbidities, lab reports
including genetic profiles and
etc)
Personalized
Knowledge Base
- Meal name
- Nutrition
- Ingredients
- Cooking style
Data collected
DATA STORE
Data collected are stored along with
domain knowledge
Image
Voice
Text
Inputs
Processing engine
Image, voice,text to
keyword
DASHBOARD
- Carbohydrate count
Trained on
Food Images
Cheesecake
NOURICH
Bitmap
Conversion
ByteArray
Conversion
3 frames per
second
Python
Script
Data Cleaning
1) Removing png
2) Removing non jpeg
Shell
Script
Image
Annotation
Training
Model: Mobile-net
model
Accuracy: 83%
Graph
protobuf file
Data preprocessing and training layer
Crawled Images
Conversion to
TFLite
Image Recognition layer
Recognized image is displayed to the user
APPLICATION ARCHITECTURE
User
Display nutrition
info for the food
Food Logs
Sources: 1) User Icon by Gregor Cresnar from the Noun Project, 2)Food Images from Google Images, 3)TensorflowLite,Nutritionix,Bash, Instagram, Google - logos are from original vendors
Matching Support Seeker (SS) with Support Provider (SP)
Online: Current application - matching SS and SP on mental
health related subReddit
https://scholarcommons.sc.edu/aii_fac_pub/516/
72
73
Modeling Exogenous Information into Epidemiological Models
(Exo-SIR)
Curve of Exogenous Information in Tamil Nadu
Infected Curve (yellow) shift left because of
Exogenous Information (Exo-SIR)
Architecture of Exo-SIR model
Simple SIR
Model
Infected
Curve for
Tamil Nadu
State
● The curve representing time to infection shifts left (28%
early) when we introduce exogenous events such as large
gatherings (eg. Tablighi Jamaat religious gathering) or labor
migration due to lockdown.
● Evaluated/validated for the impact of exogenous events on
three states in India (Rajasthan, Tamil Nadu, Kerala).
(Accepted at AI for COVID track in ACM KDD 2020)
There is a lot more:
http://aiisc.ai, http://wiki.aiisc.ai
https://scholarcommons.sc.edu/aii_fac_pub/
Many thanks to our sponsors, esp. ~10 NIH
grants (four R01s, three R21s, R56, etc) from
NIMH, NIDA, NICHD, and other institutes.

Weitere ähnliche Inhalte

Was ist angesagt?

Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...Amit Sheth
 
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Amit Sheth
 
[C&C] 의료의 미래 디지털 헬스케어
[C&C] 의료의 미래 디지털 헬스케어[C&C] 의료의 미래 디지털 헬스케어
[C&C] 의료의 미래 디지털 헬스케어Yoon Sup Choi
 
Sun==big data analytics for health care
Sun==big data analytics for health careSun==big data analytics for health care
Sun==big data analytics for health careAravindharamanan S
 
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...Artificial Intelligence Institute at UofSC
 
의료의 미래, 디지털 헬스케어
의료의 미래, 디지털 헬스케어의료의 미래, 디지털 헬스케어
의료의 미래, 디지털 헬스케어Yoon Sup Choi
 
글로벌 디지털 헬스케어 산업 및 규제 동향
글로벌 디지털 헬스케어 산업 및 규제 동향 글로벌 디지털 헬스케어 산업 및 규제 동향
글로벌 디지털 헬스케어 산업 및 규제 동향 Yoon Sup Choi
 
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면Yoon Sup Choi
 
Digital medicine comes of age - ISDM E-Newsletter Feb 2020
Digital medicine comes of age - ISDM E-Newsletter Feb 2020Digital medicine comes of age - ISDM E-Newsletter Feb 2020
Digital medicine comes of age - ISDM E-Newsletter Feb 2020David Wortley
 
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-Time
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-TimeAnalyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-Time
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-TimeEnspektos, LLC
 
Applied Artificial Intelligence & How it's Transforming Life Sciences
Applied Artificial Intelligence & How it's Transforming Life SciencesApplied Artificial Intelligence & How it's Transforming Life Sciences
Applied Artificial Intelligence & How it's Transforming Life SciencesKumaraguru Veerasamy
 
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성의료의 미래, 디지털 헬스케어 + 의료 시장의 특성
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성Yoon Sup Choi
 
디지털 헬스케어와 보험의 미래 (2019년 5월)
디지털 헬스케어와 보험의 미래 (2019년 5월)디지털 헬스케어와 보험의 미래 (2019년 5월)
디지털 헬스케어와 보험의 미래 (2019년 5월)Yoon Sup Choi
 
Will Artificial Intelligence Change Healthcare?
Will Artificial Intelligence Change Healthcare?Will Artificial Intelligence Change Healthcare?
Will Artificial Intelligence Change Healthcare?Eugene Borukhovich
 
When digital medicine becomes the medicine (1/2)
When digital medicine becomes the medicine (1/2)When digital medicine becomes the medicine (1/2)
When digital medicine becomes the medicine (1/2)Yoon Sup Choi
 
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가Yoon Sup Choi
 
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로Yoon Sup Choi
 
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로디지털 의료의 현재와 미래: 임상신경생리학을 중심으로
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로Yoon Sup Choi
 
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)Yoon Sup Choi
 

Was ist angesagt? (20)

Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...
 
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...
 
[C&C] 의료의 미래 디지털 헬스케어
[C&C] 의료의 미래 디지털 헬스케어[C&C] 의료의 미래 디지털 헬스케어
[C&C] 의료의 미래 디지털 헬스케어
 
Sun==big data analytics for health care
Sun==big data analytics for health careSun==big data analytics for health care
Sun==big data analytics for health care
 
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...
Knowledge-driven Personalized Contextual mHealth Service for Asthma Managemen...
 
kHealth Bariatrics
kHealth BariatricskHealth Bariatrics
kHealth Bariatrics
 
의료의 미래, 디지털 헬스케어
의료의 미래, 디지털 헬스케어의료의 미래, 디지털 헬스케어
의료의 미래, 디지털 헬스케어
 
글로벌 디지털 헬스케어 산업 및 규제 동향
글로벌 디지털 헬스케어 산업 및 규제 동향 글로벌 디지털 헬스케어 산업 및 규제 동향
글로벌 디지털 헬스케어 산업 및 규제 동향
 
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면
한국에서 혁신적인 디지털 헬스케어 스타트업이 탄생하려면
 
Digital medicine comes of age - ISDM E-Newsletter Feb 2020
Digital medicine comes of age - ISDM E-Newsletter Feb 2020Digital medicine comes of age - ISDM E-Newsletter Feb 2020
Digital medicine comes of age - ISDM E-Newsletter Feb 2020
 
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-Time
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-TimeAnalyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-Time
Analyzing Consumer Reaction to the Fungal Meningitis Outbreak in Real-Time
 
Applied Artificial Intelligence & How it's Transforming Life Sciences
Applied Artificial Intelligence & How it's Transforming Life SciencesApplied Artificial Intelligence & How it's Transforming Life Sciences
Applied Artificial Intelligence & How it's Transforming Life Sciences
 
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성의료의 미래, 디지털 헬스케어 + 의료 시장의 특성
의료의 미래, 디지털 헬스케어 + 의료 시장의 특성
 
디지털 헬스케어와 보험의 미래 (2019년 5월)
디지털 헬스케어와 보험의 미래 (2019년 5월)디지털 헬스케어와 보험의 미래 (2019년 5월)
디지털 헬스케어와 보험의 미래 (2019년 5월)
 
Will Artificial Intelligence Change Healthcare?
Will Artificial Intelligence Change Healthcare?Will Artificial Intelligence Change Healthcare?
Will Artificial Intelligence Change Healthcare?
 
When digital medicine becomes the medicine (1/2)
When digital medicine becomes the medicine (1/2)When digital medicine becomes the medicine (1/2)
When digital medicine becomes the medicine (1/2)
 
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가
의료 인공지능: 인공지능은 의료를 어떻게 혁신하는가
 
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
 
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로디지털 의료의 현재와 미래: 임상신경생리학을 중심으로
디지털 의료의 현재와 미래: 임상신경생리학을 중심으로
 
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)
인공지능은 의료를 어떻게 혁신하는가 (2019년 3월)
 

Ähnlich wie AI & Healthcare @ AIISC: May 2021 Snapshot

Augmented Personalized Health: using AI techniques on semantically integrated...
Augmented Personalized Health: using AI techniques on semantically integrated...Augmented Personalized Health: using AI techniques on semantically integrated...
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
 
Improving health care outcomes with responsible data science
Improving health care outcomes with responsible data scienceImproving health care outcomes with responsible data science
Improving health care outcomes with responsible data scienceWessel Kraaij
 
Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersKno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
 
future of health UMCG 3 june1630-a safe society
future of health UMCG 3 june1630-a safe societyfuture of health UMCG 3 june1630-a safe society
future of health UMCG 3 june1630-a safe societyLisette Van Gemert-Pijnen
 
Trusted! Quest for data-driven and fair health solutions
Trusted! Quest for data-driven and fair health solutions Trusted! Quest for data-driven and fair health solutions
Trusted! Quest for data-driven and fair health solutions Sitra / Hyvinvointi
 
Role of AI in Transforming the Healthcare Industry
Role of AI in Transforming the Healthcare IndustryRole of AI in Transforming the Healthcare Industry
Role of AI in Transforming the Healthcare IndustryHammadAfzal23
 
Health Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxHealth Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxArti Parab Academics
 
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...Exploiting Multimodal Information for Machine Intelligence and Natural Intera...
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...Artificial Intelligence Institute at UofSC
 
From personal health data to a personalized advice
From personal health data to a personalized adviceFrom personal health data to a personalized advice
From personal health data to a personalized adviceWessel Kraaij
 
ppt for data science slideshare.pptx
ppt for data science slideshare.pptxppt for data science slideshare.pptx
ppt for data science slideshare.pptxMangeshPatil358834
 
The future interface of mental health with information technology: high touch...
The future interface of mental health with information technology: high touch...The future interface of mental health with information technology: high touch...
The future interface of mental health with information technology: high touch...HealthXn
 
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018Julien VENNE
 
E-health means participatory health: how social, mobile, wearable and ambient...
E-health means participatory health: how social, mobile, wearable and ambient...E-health means participatory health: how social, mobile, wearable and ambient...
E-health means participatory health: how social, mobile, wearable and ambient...Kathleen Gray
 
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...Pei-Yun Sabrina Hsueh
 
Web 2.0 systems supporting childhood chronic disease management: a general ar...
Web 2.0 systems supporting childhood chronic disease management: a general ar...Web 2.0 systems supporting childhood chronic disease management: a general ar...
Web 2.0 systems supporting childhood chronic disease management: a general ar...Gunther Eysenbach
 

Ähnlich wie AI & Healthcare @ AIISC: May 2021 Snapshot (20)

Augmented Personalized Health: using AI techniques on semantically integrated...
Augmented Personalized Health: using AI techniques on semantically integrated...Augmented Personalized Health: using AI techniques on semantically integrated...
Augmented Personalized Health: using AI techniques on semantically integrated...
 
Improving health care outcomes with responsible data science
Improving health care outcomes with responsible data scienceImproving health care outcomes with responsible data science
Improving health care outcomes with responsible data science
 
Geohealth symposium-UTen ITC
Geohealth symposium-UTen ITCGeohealth symposium-UTen ITC
Geohealth symposium-UTen ITC
 
Geohealth and Safe Society
Geohealth and Safe SocietyGeohealth and Safe Society
Geohealth and Safe Society
 
Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersKno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
 
future of health UMCG 3 june1630-a safe society
future of health UMCG 3 june1630-a safe societyfuture of health UMCG 3 june1630-a safe society
future of health UMCG 3 june1630-a safe society
 
Trusted! Quest for data-driven and fair health solutions
Trusted! Quest for data-driven and fair health solutions Trusted! Quest for data-driven and fair health solutions
Trusted! Quest for data-driven and fair health solutions
 
Role of AI in Transforming the Healthcare Industry
Role of AI in Transforming the Healthcare IndustryRole of AI in Transforming the Healthcare Industry
Role of AI in Transforming the Healthcare Industry
 
Integrated health monitoring
Integrated health monitoringIntegrated health monitoring
Integrated health monitoring
 
Precision and Participatory Medicine - MEDINFO 2015 Panel on big data
Precision and Participatory Medicine - MEDINFO 2015 Panel on big dataPrecision and Participatory Medicine - MEDINFO 2015 Panel on big data
Precision and Participatory Medicine - MEDINFO 2015 Panel on big data
 
MedInfo 2010 Report
MedInfo 2010 ReportMedInfo 2010 Report
MedInfo 2010 Report
 
Health Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxHealth Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptx
 
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...Exploiting Multimodal Information for Machine Intelligence and Natural Intera...
Exploiting Multimodal Information for Machine Intelligence and Natural Intera...
 
From personal health data to a personalized advice
From personal health data to a personalized adviceFrom personal health data to a personalized advice
From personal health data to a personalized advice
 
ppt for data science slideshare.pptx
ppt for data science slideshare.pptxppt for data science slideshare.pptx
ppt for data science slideshare.pptx
 
The future interface of mental health with information technology: high touch...
The future interface of mental health with information technology: high touch...The future interface of mental health with information technology: high touch...
The future interface of mental health with information technology: high touch...
 
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018
The Digital Health Society (by Julien Venne) @ICT2018 Vienna 6th Dec 2018
 
E-health means participatory health: how social, mobile, wearable and ambient...
E-health means participatory health: how social, mobile, wearable and ambient...E-health means participatory health: how social, mobile, wearable and ambient...
E-health means participatory health: how social, mobile, wearable and ambient...
 
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
 
Web 2.0 systems supporting childhood chronic disease management: a general ar...
Web 2.0 systems supporting childhood chronic disease management: a general ar...Web 2.0 systems supporting childhood chronic disease management: a general ar...
Web 2.0 systems supporting childhood chronic disease management: a general ar...
 

Kürzlich hochgeladen

person with disability and pwd act ppt.pptx
person with disability and pwd act ppt.pptxperson with disability and pwd act ppt.pptx
person with disability and pwd act ppt.pptxMUKESH PADMANABHAN
 
Artificial Intelligence in Healthcare: Challenges, Risks, Benefits
Artificial Intelligence in Healthcare: Challenges, Risks, BenefitsArtificial Intelligence in Healthcare: Challenges, Risks, Benefits
Artificial Intelligence in Healthcare: Challenges, Risks, BenefitsIris Thiele Isip-Tan
 
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHY
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHYCECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHY
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHYRMC
 
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdfCompliatric Where Compliance Happens
 
Keeping Your Bones Healthy – A Comprehensive Guide.
Keeping Your Bones Healthy – A Comprehensive Guide.Keeping Your Bones Healthy – A Comprehensive Guide.
Keeping Your Bones Healthy – A Comprehensive Guide.Gokuldas Hospital
 
Three Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborThree Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborHealth Catalyst
 
Eating Disorders in Athletes I Sports Psychology
Eating Disorders in Athletes I Sports PsychologyEating Disorders in Athletes I Sports Psychology
Eating Disorders in Athletes I Sports Psychologyshantisphysio
 
Identifying Signs of Mental Health Presentation (1).pptx
Identifying Signs of Mental Health Presentation (1).pptxIdentifying Signs of Mental Health Presentation (1).pptx
Identifying Signs of Mental Health Presentation (1).pptxsandhulove46637
 
Common ENT Problems and Their Solutions at Miracles Healthcare
Common ENT Problems and Their Solutions at Miracles HealthcareCommon ENT Problems and Their Solutions at Miracles Healthcare
Common ENT Problems and Their Solutions at Miracles HealthcareMiracles Healthcare
 
Diseases of the Respiratory System (J00-J99),.pptx
Diseases of the Respiratory System (J00-J99),.pptxDiseases of the Respiratory System (J00-J99),.pptx
Diseases of the Respiratory System (J00-J99),.pptxEMADABATHINI PRABHU TEJA
 
Assisted Living Care Residency - PapayaCare
Assisted Living Care Residency - PapayaCareAssisted Living Care Residency - PapayaCare
Assisted Living Care Residency - PapayaCareratilalthakkar704
 
Kamada - Q42023 Results Presentation - March 2024
Kamada - Q42023 Results Presentation - March 2024Kamada - Q42023 Results Presentation - March 2024
Kamada - Q42023 Results Presentation - March 2024KAMADA
 
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdfDolisha Warbi
 
LARYNGEAL CANCER.pptx Prepared by Neha Kewat
LARYNGEAL CANCER.pptx  Prepared by Neha KewatLARYNGEAL CANCER.pptx  Prepared by Neha Kewat
LARYNGEAL CANCER.pptx Prepared by Neha KewatNehaKewat
 
Empathy Is a Stress Response - Choose Compassion instead
Empathy Is a Stress Response - Choose Compassion insteadEmpathy Is a Stress Response - Choose Compassion instead
Empathy Is a Stress Response - Choose Compassion insteadAlex Clapson
 
ACCA Version of AI & Healthcare: An Overview for the Curious
ACCA Version of AI & Healthcare: An Overview for the CuriousACCA Version of AI & Healthcare: An Overview for the Curious
ACCA Version of AI & Healthcare: An Overview for the CuriousKR_Barker
 
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTS
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTSNERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTS
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTSWINCY THIRUMURUGAN
 
ARTHRITIS.pptx Prepared by monika gopal Tutor
ARTHRITIS.pptx Prepared  by monika gopal TutorARTHRITIS.pptx Prepared  by monika gopal Tutor
ARTHRITIS.pptx Prepared by monika gopal TutorNehaKewat
 
Physiology of NERVE IMPULSE and action potential
Physiology of NERVE IMPULSE and action potentialPhysiology of NERVE IMPULSE and action potential
Physiology of NERVE IMPULSE and action potentialKeertis1
 

Kürzlich hochgeladen (20)

person with disability and pwd act ppt.pptx
person with disability and pwd act ppt.pptxperson with disability and pwd act ppt.pptx
person with disability and pwd act ppt.pptx
 
Artificial Intelligence in Healthcare: Challenges, Risks, Benefits
Artificial Intelligence in Healthcare: Challenges, Risks, BenefitsArtificial Intelligence in Healthcare: Challenges, Risks, Benefits
Artificial Intelligence in Healthcare: Challenges, Risks, Benefits
 
Annual Training
Annual TrainingAnnual Training
Annual Training
 
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHY
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHYCECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHY
CECT NECK NECK ANGIOGRAPHY CAROTID ANGIOGRAPHY
 
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf
2024 Compliatric Webianr Series - Contracts and MOUs from a HRSA Perspective.pdf
 
Keeping Your Bones Healthy – A Comprehensive Guide.
Keeping Your Bones Healthy – A Comprehensive Guide.Keeping Your Bones Healthy – A Comprehensive Guide.
Keeping Your Bones Healthy – A Comprehensive Guide.
 
Three Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborThree Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and Labor
 
Eating Disorders in Athletes I Sports Psychology
Eating Disorders in Athletes I Sports PsychologyEating Disorders in Athletes I Sports Psychology
Eating Disorders in Athletes I Sports Psychology
 
Identifying Signs of Mental Health Presentation (1).pptx
Identifying Signs of Mental Health Presentation (1).pptxIdentifying Signs of Mental Health Presentation (1).pptx
Identifying Signs of Mental Health Presentation (1).pptx
 
Common ENT Problems and Their Solutions at Miracles Healthcare
Common ENT Problems and Their Solutions at Miracles HealthcareCommon ENT Problems and Their Solutions at Miracles Healthcare
Common ENT Problems and Their Solutions at Miracles Healthcare
 
Diseases of the Respiratory System (J00-J99),.pptx
Diseases of the Respiratory System (J00-J99),.pptxDiseases of the Respiratory System (J00-J99),.pptx
Diseases of the Respiratory System (J00-J99),.pptx
 
Assisted Living Care Residency - PapayaCare
Assisted Living Care Residency - PapayaCareAssisted Living Care Residency - PapayaCare
Assisted Living Care Residency - PapayaCare
 
Kamada - Q42023 Results Presentation - March 2024
Kamada - Q42023 Results Presentation - March 2024Kamada - Q42023 Results Presentation - March 2024
Kamada - Q42023 Results Presentation - March 2024
 
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf
"ANATOMY AND PHYSIOLOGY OF THE SKIN".pdf
 
LARYNGEAL CANCER.pptx Prepared by Neha Kewat
LARYNGEAL CANCER.pptx  Prepared by Neha KewatLARYNGEAL CANCER.pptx  Prepared by Neha Kewat
LARYNGEAL CANCER.pptx Prepared by Neha Kewat
 
Empathy Is a Stress Response - Choose Compassion instead
Empathy Is a Stress Response - Choose Compassion insteadEmpathy Is a Stress Response - Choose Compassion instead
Empathy Is a Stress Response - Choose Compassion instead
 
ACCA Version of AI & Healthcare: An Overview for the Curious
ACCA Version of AI & Healthcare: An Overview for the CuriousACCA Version of AI & Healthcare: An Overview for the Curious
ACCA Version of AI & Healthcare: An Overview for the Curious
 
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTS
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTSNERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTS
NERVE CELLS FINAL( NEURON AND GLIAL CELLS.pptx FOR NURSING STUDENTS
 
ARTHRITIS.pptx Prepared by monika gopal Tutor
ARTHRITIS.pptx Prepared  by monika gopal TutorARTHRITIS.pptx Prepared  by monika gopal Tutor
ARTHRITIS.pptx Prepared by monika gopal Tutor
 
Physiology of NERVE IMPULSE and action potential
Physiology of NERVE IMPULSE and action potentialPhysiology of NERVE IMPULSE and action potential
Physiology of NERVE IMPULSE and action potential
 

AI & Healthcare @ AIISC: May 2021 Snapshot

  • 1. Healthcare Research @ AI Institute: dHealth, public health, epidemiology, biomedicine Overview Presentation to the MUSC’s AI Hub and others May 2021 Amit Sheth, Founding Director http://aiisc.ai
  • 2. © 2021 NAVER. All rights reserved.
  • 3. “ 3 AIISC in core AI areas, and interdisciplinary AI/AI applications >> 25 researchers including 4 faculty (6 in Fall 2021), 2-3 postdocs, ~20 PhD students, >10 MS/BS and several interns/associates
  • 4. © 2021 NAVER. All rights reserved.
  • 5. D-Health, health informatics, public health, epidemiology – sample collaborations (Pending & PLANNED Submissions only) •College of Medicine/Prisma/Prisma-Upstate •Mental health [M Natarajan], Addiction [A. Litwin], Asthma [R. Dawson], Diabetes & Obesity [L. Knight], Hypertension - Diet & Nutrition [S. Donevant], Neutropenia [S. Craemer] •College of Pharmacy: EOCRC [P. Backhaults, L. Hofseth, et al] •College of Nursing: COVID-19 mobile app [R. Hughes, S. Donevant], mental health chatbot [R. Hughes, S. Donevant, P. Raynor] •Arnold School of Public Health: Mental Health [S. Qiao], Healthcare Big Data - Education & Training [X. Li, et. al.] •USCAND, Inst of Mind & Brain: Neuroscience [R. Desai] and neurodevelopmental diseases [J. Bradshaw, J. Roberts] •CEC- IIT, BME, CSE: Health IT/Smart Health [E. Regan], UI/UX [D. Wu]; Health mApp [N. Boltin], (several in CSE).
  • 6. Projects ➢ KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care (NICHD) ➢ mHealth to Improve Carbohydrate Counting Accuracy in Pediatric Type 1 Diabetes ➢ Improving mental health of COVID-19 patients with an Artificial Intelligence-based chatbot ➢ Personalized Virtual Health Assistant Enabled by Knowledge-infused Reinforcement Learning for Adaptive Mental Health Self-management ➢ Characterizing and supporting help seekers on social media using expert-in-the-loop learning ➢ Modeling Social Behavior for Healthcare Utilization in Depression (NIMH) ➢ eDarkTrends : Monitoring Cryptomarkets to Identify Emerging Trends of Illicit Synthetic Opioids Use (NIDA) ➢ Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use (NIDA) ➢ Innovative NIDA National Early Warning System Network (iN3) (NIDA) ➢ PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology (NIDA) ➢ Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest (NSF) ➢ Discrepancies in Diagnosis and Treatment of Cardiovascular Disease Based on Sex and Gender to Improve Women’s Health (NHLBI) ➢ Early Onset of Colorectal Cancer ➢ Digestive Inflation Index ➢ more ...
  • 7. Types of Healthcare Data ◎ EMR ◎ Social Media (Reddit, Twitter,Web Forums) ◎ Conversations: Patient- Clinician, Virtual Health Assistant-Patient ◎ Patient Generated: Wearable/sensor data, mApp data ◎ Images: food, fMRI AI Techniques and Technologies ◎ Knowledge Graphs/Ontologies (contextualization, personalization, abstraction) ◎ NLP/NLU ◎ Machine Learning/Deep Learning (RL, GAN, CNN, LSTM,....) ◎ Conversational AI, Q/A ◎ mApp, Virtual Health Assistants (Chatbots) ◎ Health sensors/IoTs/mobile devices
  • 8. Medical Conditions/Healthcare Challenges addressed ◎ Asthma ◎ Mental Health ◎ Addiction ◎ COVID-19 ◎ Cardiovascular Disease ◎ Type 1 Diabetes in Children ◎ Adult Diabetes - Hypertension ◎ Neutropenia ◎ Sleep Disorders ◎ Gender and Race Disparity ◎ Demographics ◎ SDOH ◎ Drug Design Partners: Weill Cornell, UCSF Medical, Prisma-Health, Addiction Research Center, UofSC Medical/Pharma/Public Health/Nursing; Wright State Physicians, ….
  • 9. Unique Value Propositions and Strengths [for Health Apps] ◎ Development of Knowledge Graphs ◎ Knowledge-infused (Deep) Learning and Knowledge- infused NLP: Explainable AI ◎ Conversational AI/collaborative agents ◎ Augmented Personalized Health
  • 10. 10 Health Knowledge Graph Drug Abuse Ontology Lokala U, Daniulaityte R, Lamy F, Gaur M, Thirunarayan K, Kursuncu U, Sheth. A. (2020). DAO: An ontology for substance use epidemiology on social media and dark web. JMIR. https://doi.org/10.2196/preprints.24938 https://scholarcommons.sc.edu/aii_fac_pub/356/ [Shah and Sheth US patent 2015]
  • 11. Knowledge-Infused Learning with Semantic, Cognitive, Perceptual Computing Framework 11 Overarching Theory Knowledge Domain (Ontology) Personalized KG Multisensory Sensing & Multimodal Data Interactions Images Text Speech Videos IoTs Natural Language Processing, Machine with Deep Learning AUGMENTED PERSONALIZED HEALTH (APH) Modeling broader disease context, and personalized user behavior Reasoning & decision- making framework Minimize data overload, assist in making choices, appraisal, recommendations TEDx talk: Augmented Health with Personalized Data and AI
  • 12. 12 Contextualization and Personalization kBOT initiates greeting conversation. Understands the patient’s health condition (allergic reaction to high ragweed pollen level) via the personalized patient’s knowledge graph generated from EMR, PGHD, and prior interactions with the kBot. Generates predictions or recommended course of actions. Inference based on patient’s historical records and background health knowledge graph containing contextualized (domain-specific) knowledge. Figure: Example kBot conversation which utilizes background health knowledge graph and patient’s knowledge graph to infer and generate recommendation to patients. ★ Conversing only information relevant to the patient Context enabled by relevant healthcare knowledge including clinical protocols.
  • 13. Why Knowledge Infused Learning (K-IL)? By changing the inputs, it can enrich the representation (E.g. Radicalization on Social Media) By changing parameters, we can control the learned patterns/correlations to adhere to the knowledge. Deep Infusion would allow us finer grained control over learned patterns to ensure adherence to knowledge at every step of the hierarchy Explanations easy to derive from the KG used 13 Contextual Modeling to analyze Radicalization on Social Media (Hate)
  • 14. Health-e Gamecock COVID-19 daily status check (on Apple Store)
  • 16. What? Comprehensive, Customizable, Adaptive ● App/chatbot for individuals ○ Cohort 1: College of Nursing (testing, research) ○ Cohort 2: Students, Staff, Faculty ● Dashboards for different uses: general administration, health services, research ○ Support for custom protocol Gamecock look-n-feel, easy to use and engaging, customizable, secure, privacy disclosure/management, HIPAA compliance, accessibility (ADA compliance), scalable, well tested, extensible
  • 17. Why? Proactively keep Campus healthy ● Informing and Educating the Community, Regularly Check Health Status: ○ Stay informed and educated, make better individual decisions, feel safer, reduce adverse outcomes ○ In case of concern, connect with Student Health Services, manage isolation protocols ● Providing Campus-wide View: tools needed to make campus-wide decisions ○ Insurance against possible adverse outcome from low-risk approach (CDC’s Considerations for Institutes of Higher Education (May 21), proactive implementation of protocols ● Research: COVID-19, Mental Health
  • 18. How? ● Comprehensive campus-wide involvement and coordination: requirements, development, testing, operations ○ College of Nursing: lead evaluation ○ Student Health Services ○ School of Public Health ○ Division of Information Technology ○ CEC; of course, the AI Institute ● Development team with extensive experience ● Much more functional, forward looking, and cheaper than vendors
  • 20. Demo: Health-e Gamecock COVID-19 App (WebApp, IOS, Android) Note: development is now complete, app is evaluated and we plan to use it for a major study.
  • 21. Health-e Gamecock COVID-19 daily status check (on Apple Store)
  • 22. Augmented Personalized Health Check out the TEDx talk and the original article 22
  • 24. 24 Use Case: kHealth Asthma Many Sources of Highly Diverse Data (& collection methods: Active + Passive): Up to 1852 data points/ patient /day http://bit.ly/kHealth-Asthma kBot with screen interface for conversation Images Text Speech ★ Episodic to Continuous Monitoring ★ Clinician-centric to Patient-centric ★ Clinician controlled to Patient-empowered ★ Disease Focused to Wellness-focused ★ Sparse data to Multimodal Big Data *(Asthma-Obesity)
  • 25. Self Monitoring with kHealthDash: Knowledge enabled personalized DASHboard for Asthma Management Video link - https://youtu.be/yUgXCPwc55M
  • 26. Digital Phenotype Score vs Asthma Control Test Score Digital Phenotype Score = Symptom Score + Rescue Score + Activity Score + Awakening Score
  • 27. 27 Utkarshani Jaimini, Krishnaprasad Thirunarayan, Maninder Kalra, Revathy Venkataramanan, Dipesh Kadariya, Amit Sheth, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma”, JMIR Pediatr Parent 2018;1(2):e11988, DOI: 10.2196/11988.
  • 28. Self Appraisal with Digital Phenotype Score Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma, JMIR Pediatric Parent 2018;1(2):e11988 https://medium.com/leoilab/digital-phenotyping-turning-our-smartphones-inward-141a75b2f2a3 ● Digital Phenotype Score (DPS) is defined as the score to quantify the digital phenotypes collected from the social media, smartphones, wearables, and sensors streams. ● DPS acts as a cumulative measure for the abstraction of knowledge and information from the raw digital phenotypic data. ● The integration of the DPS can enable personalized interventions in real time which are directly responsive to the healthcare need of a patient.
  • 29. Using Knowledge Graphs to construct a contextualized and personalized profile for each patient that can drive insights and personalized care strategies
  • 30. ● Published in ISWC 2018 Contextualized Knowledge Graph Workshop, 2018. Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth 2018, ISWC. ● Sheth, A., Jaimini, U., & Yip, H. Y. (2018). How Will the Internet of Things Enable Augmented Personalized Health? IEEE Intelligent Systems, 33(1), 89–97. https://doi.org/10.1109/MIS.2018.012001556
  • 31. Determining Personalized Asthma Triggers: Seasonal Dependency Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
  • 32. © 2021 NAVER. All rights reserved.
  • 33. Evidence based Path to Personalization Patient-A was monitored for 13 weeks encompassing winter to spring 2018. Type: Severe, low medication compliance. Absence of Pollen First 6 weeks Presence of Pollen Rest of the 7 weeks Pre (observe) 4 weeks Post (validate) 2 weeks Pre 4 weeks Post 3 weeks Pollen 0 Pollen 0 days Pollen 17 days Pollen 3 days PM2.5 20 days PM2.5 5 days PM2.5 14 days PM2.5 2 days Ozone 1 day Ozone 0 Ozone 0 Ozone 1 day Asthma Episodes* 21 days Asthma Episodes 5 days Asthma Episodes 17 days Asthma Episodes 3 days ● Absence of Pollen - PM2.5 is the trigger ● Presence of Pollen - Pollen and PM2.5. Severe symptoms occurred in this period. Presence of both PM2.5 and Pollen increased the intensity of asthma episodes. Venkataramanan R, Thirunarayan K, Jaimini U, Kadariya D, Yip HY, Kalra M, Sheth A. Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study. JMIR Pediatr Parent 2019;2(1):e14300. doi: 10.2196/14300 PMID: 31518318 PMCID: 6716491
  • 34. Interventional Strategy Actionable insights using Digital Phenotype Score and Controller Compliance Score
  • 36. 36 Q1: Do you feel restless in sleep? A1: Two-three times a week Q2: Do you wake early? How often does it happen? A2: Too early, happens two times a week. Q3: Do you persistently feel sad? A3: Yes. A sense of loneliness Q4: How often you stay alone? A4: I am divorced. Q5: Have you been diagnosed with PTSD? A5: No Q6: Have you seen an MHP for your anxiety disorder? A6: hmm recently. Participant was asked do they feel restless in sleep, then participant said hmm,two three times week Participant was asked, do they persistently feel sad, participant said divorce loneliness Participant was asked, have they been diagnosed with PTSD, participant said hmm recently. Diagnostic Interview Summaries Contextualized Diagnosis of patient conversations Correct diagnosis: Post Traumatic Stress Disorder
  • 41. Online Mental Health Support Social Media: Reddit 41
  • 42. Interest We are interested in Matching Support Seekers -SSs (left) with Support Providers - SPs (right) Current State Currently, moderators (center) do this matching 42 Proposal Our AI system will replace/assist the moderators that use medical knowledge, information about the user, extracted from the posts to perform this matching
  • 44. Using knowledge and lexicons with deep learning for matching users on Reddit
  • 46. Addiction (Opioid, Cannabis, Synthetic Cannabinoid, Prescription Drug Abuse) X Epidemiology 46
  • 48. Knowledge Graph for better Information Extraction: Application in Epidemiology 48 Cameron, Delroy, Gary A. Smith, Raminta Daniulaityte, Amit P. Sheth, Drashti Dave, Lu Chen, Gaurish Anand, Robert Carlson, Kera Z. Watkins, and Russel Falck. "PREDOSE: a semantic web platform for drug abuse epidemiology using social media." Journal of biomedical informatics 46, no. 6 (2013): 985-997.
  • 49. Knowledge-infused via DAO Ontology for solving relation between Cannabis and Depression
  • 50. K-IL: Shallow Infusion with DAO in DL model to detect trends in cryptomarkets Table: Sample properties derived from cryptomarket with DAO
  • 51. Motivation ● The opioid epidemic entrenched in Ohio and the Midwest of the US. ● The prevalence of opioid and its impact on the well-being of individuals and the society in Ohio. ○ Mental Health & Suicide Risk Questions 1. How can we use social media to measure mental health impact of opioid prevalence? 1. Are there association between opioid and mental health/suicide risk based on social media data? Approach Monitoring the prevalence of opioid and its impact on mental health and suicide in Ohio, utilizing a scalable knowledge and data driven BIGDATA (BD) approach via social media. BD Spoke: Opioid and Substance Use in Ohio
  • 52. Score Calculation Opioid Mental Health Depression Addiction Suicide Risk Ideation, Behavior Attempt Correlations ● Sheth, Amit, and Pavan Kapanipathi. "Semantic filtering for social data." IEEE Internet Computing 20, no. 4 (2016): 74-78. ● Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, and Amit Sheth. 2018. Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models. In Proceedings of the 27th International Conference on Computational Linguistics (COLING2018), pages 1986-1997. Association for Computational Linguistics ● Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018, October). " Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 753- 762). ● Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., ... & Pathak, J. (2019, May). Knowledge-aware assessment of severity of suicide risk for early intervention. In The World Wide Web Conference (pp. 514-525). ● Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., ... & Sheth, A. (2017, July). Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 1191-1198). ● Daniulaityte, R., Nahhas, R. W., Wijeratne, S., Carlson, R. G., Lamy, F. R., Martins, S. S., ... & Sheth, A. (2015). “Time for dabs”: Analyzing Twitter data on marijuana concentrates across News Articles Twitter Data Domain Knowledg e Content Enrichment DAO DSM-5 Location Extraction Keyphrase Extraction Age-based Clustering Semantic Filtering Entity Extraction NLM Training f(.) Knowledge Infused Natural Language Processing (Ki-NLP) Semantic Mapping Semantic Proximity Topic Model Language Model DAO DSM-5 Dashboard Visualizations (Online) Offline Analysis & Visualizations BD Spoke: Opioid and Substance Use in Ohio
  • 53. ● Substance use addictive disorder linked to opioid with higher correlation. ● Gender dysphoria, Dissociative and OCD disorders are correlating moderately. Opioid Prevalence in Ohio vs. Mental Health & Suicide ● Suicide ideation (initial stage) with highest correlation. ● Mild severity level of suicide risk linked to higher correlation. ● Weak correlation for suicide indication (before initial) p N counties p N counties
  • 54. with a Social Quality Index Insights from semantic analysis of Social Media Big Data Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index Insights from semantic analysis of Social Media Big Data ○ Mental Health: Depression, Anxiety ○ Addiction: Substance use/abuse ○ Gender-based/Domestic Violence ○ Mental Health: Depression, Anxiety ○ Addiction: Substance use/abuse ○ Gender-based/Domestic Violence
  • 55. Capability Demonstration 2 ● Spatio-temporal analysis of big data (1billion tweets, 700,000 articles) ● Use of domain knowledge graphs (mental health, addiction) ● Complex language understanding (slang, specialized domain terms) ○ Can do people/demographic, network, sentiment, emotion, intent analysis ● Scenario: Understand the implication of policy choices (e.g., school/business closing) and real- world events during COVID-19 55
  • 56. A calculated Social Quality Index (SQI) aggregates mental health components (Depression, Anxiety), Addiction and Substance Use Disorders. Social Quality Index (SQI) vecteezy.com ● Change in SQI informs comparisons between states. ● Raw transformed SQI into relative state rankings changing over time.
  • 57. e.g., IN, NH, OH, OR, WA, WY are worsening. Results: Relative State Rankings Reveal Patterns SQI Ranking April 4 - 10 SQI Ranking March 14 - 20 SQI Ranking March 21 - 27 SQI Ranking March 23-April 3 Darker: Better Social Quality
  • 58. Results: Cluster --Improving SQI Ranking SQI bad SQI better SQI better SQI better Frequency Depression: 125037 Addiction: 92897 Anxiety: 81891 Total: 299825 Frequency Depression: 113830 Addiction: 81810 Anxiety: 74080 Total: 269720 Frequency Depression: 81463 Addiction: 60166 Anxiety: 45998 Total: 187627 Frequency Depression: 59088 Addiction: 49086 Anxiety: 46887 Total: 155061 IL, NY, MD, AZ, NM, MA. March 14-20 March 21-27 March 28-April 3 April 4-10
  • 59. External Events ● Stay at home order ● Extension non-essential closure ● Closing parks ● State of emergency ● School Closure ● Mental Health Alarm ● Extension of small business closure ● Bill payment deadline extended ● 41k new job openings ● Child-care assistance for essential workers ● Spike in number of cases ● Stay at home order extended ● Extension School Closure ● State of emergency Extension ● Unemployment Increase(>800%) ● Tax deadline extended ● SNAP Benefits ● Death Benefits ● Domestic Violence Alarm ● Spike in number of cases ● Stay at home order extended ● Extension School Closure ● State of emergency Extension ● Closure barber shops and related businesses ● Number of deaths cross 50000 ● Unemployment Increase(>2.5k%) ● Tax deadline extended ● Phases reopening ● Limited indoor seating or gathering ● CARES Act Change in SQI relative to first week
  • 63. Construction of Personalized Knowledge Graph:
  • 64. Knowledge-infused Reinforcement Learning ● The input to the agent is sequential through many steps, it gets an input and a reward at every step and learns the right output gradually through reinforcement.
  • 65. APH Self Management: Reinforcement Learning
  • 66. NOURICH A system to monitor diet, recommend meals and promote healthy eating habits: Current application: Type 1 diabetes in Children Contact: Revathy Venkataramanan Acknowledgement: Thanks to my collaborators Hong Yung Yip and Thilini Wijayasriwartane for the slides NOURICH Know What you Eat
  • 67. Overweight Obesity Hospitalization Prevent overweight moving to obesity Prevent obesity leading to hospitalization Bridging the gap “Focusing on reducing excess and impulsive calorie consumption and making an informed decision about food choices and physical activity can help one attain a healthier weight and minimize the risk of chronic illness” The Dietary Guidelines of Americans 2010
  • 68. ● Real-time food recognition ● Tensorflow Mobile net model 20 Food Categories 700 Images/Category Image Data Source Image Source: https://commons.wikimedia.org/wiki/File:Google_%22G%22_Logo.svg, https://freepik.com Nutrition Management System NOURICH GOAL: A system to monitor diet, recommend meals and promote healthy eating habits.
  • 69. Architecture for application to Type 1 Diabetes in Children Data sources - User specific (food allergies, comorbidities, lab reports including genetic profiles and etc) Personalized Knowledge Base - Meal name - Nutrition - Ingredients - Cooking style Data collected DATA STORE Data collected are stored along with domain knowledge Image Voice Text Inputs Processing engine Image, voice,text to keyword DASHBOARD - Carbohydrate count
  • 70. Trained on Food Images Cheesecake NOURICH Bitmap Conversion ByteArray Conversion 3 frames per second Python Script Data Cleaning 1) Removing png 2) Removing non jpeg Shell Script Image Annotation Training Model: Mobile-net model Accuracy: 83% Graph protobuf file Data preprocessing and training layer Crawled Images Conversion to TFLite Image Recognition layer Recognized image is displayed to the user APPLICATION ARCHITECTURE User Display nutrition info for the food Food Logs Sources: 1) User Icon by Gregor Cresnar from the Noun Project, 2)Food Images from Google Images, 3)TensorflowLite,Nutritionix,Bash, Instagram, Google - logos are from original vendors
  • 71. Matching Support Seeker (SS) with Support Provider (SP) Online: Current application - matching SS and SP on mental health related subReddit https://scholarcommons.sc.edu/aii_fac_pub/516/
  • 72. 72
  • 73. 73 Modeling Exogenous Information into Epidemiological Models (Exo-SIR) Curve of Exogenous Information in Tamil Nadu Infected Curve (yellow) shift left because of Exogenous Information (Exo-SIR) Architecture of Exo-SIR model Simple SIR Model Infected Curve for Tamil Nadu State ● The curve representing time to infection shifts left (28% early) when we introduce exogenous events such as large gatherings (eg. Tablighi Jamaat religious gathering) or labor migration due to lockdown. ● Evaluated/validated for the impact of exogenous events on three states in India (Rajasthan, Tamil Nadu, Kerala). (Accepted at AI for COVID track in ACM KDD 2020)
  • 74. There is a lot more: http://aiisc.ai, http://wiki.aiisc.ai https://scholarcommons.sc.edu/aii_fac_pub/ Many thanks to our sponsors, esp. ~10 NIH grants (four R01s, three R21s, R56, etc) from NIMH, NIDA, NICHD, and other institutes.