임상의사 관점의 의료빅데이터 연구와 임상적용 - From Clinic To Data, From Data To Clinic

임상의사 관점의 의료빅데이터 연구와 임상적용 From Clinic to Data, From Data to Clinic

Contents
1.What is Healthcare Big Data?
2.Healthcare Big Data
①Electrical Health Records (EHR)
Structured/Unstructured Data
②Medical Images
③National Healthcare Data
④Behavior/Sensor Data
⑤Genetic Data
3.Clinical and Research Applications

Architecture of Big Data Analytics
2014 Big data analytics in healthcare- promise and potential

Machine Learning
2014 Big data bioinformatics

Tipping Point for Big Data Healthcare
2013 McKinsey The big data revolution in healthcare

Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

Candidate Gene
Approach
Genome-wide
Approach
Choose a Gene
from Prior Knowledge
Analyze the Gene
Analyze ALL Genes
Discover Novel Findings

GWAS (Genome wide association study)
SNP chip Whole Genome SNP Profiling (500K~1M SNPs)
Common Variant

Estrada et al., Nature Genetics, 2012
+ novel targets
for bone biology
Recent largest GWAS
GEFOS consortium

2010 An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

GWAS  PheWAS Phenotype-wide Association Study

1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Marylyn D. Ritchie

Genomics
Wearable/ Smart Device
Medical Informatics
Genome Medicine
Quantified Self
Health Avatar
Electronic Medical Records (EMR)
Medical Images (MRI, CT)
National Healthcare data (Insurance)

Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care

Common EHR Data
Joshua C. Denny Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoS Comput Biol. 2012 December; 8(12):
International Classification of Diseases (ICD)
Current Procedural Terminology (CPT)

Big Data Analysis
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8 1/Creatinine
한 환자의 10년간 신장기능의 변화
전체 환자의 당 조절 정도 분포
충북대 정보통계학과
허태영 교수

PCA Analysis
혈당
신장기능

밤동안 저혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현기증, 공복감, 두통, 피로감등의 저혈당 에 저혈당 이 있을 즉알려주도록 밤사이 특이호소 수면유지상처와 통증 상처부위 출혈 oozing, severe pain 알리도록 고혈당 처방된 당뇨식이의 중요성과 간식을 자제하도록 .고혈당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위 oozing Rt.foot rolling keep드레싱 상태를 고혈당 고혈당 의식변화 BST 387 checked.고혈당으로 인한 구강 내 감염 위해 식후 양치, gargle 등 구강 위생 격려.당뇨환자의 발관리 방법에 . 목표 혈당, 목표 당화혈색소에 .식사를 거르거나 지연하지 않도록 .식사요법, 운동요법, 약물요법을 정확히 지키는 것이 중요을 .처방된 당뇨식이의 중요성과 간식을 자제하도록 .고혈당 ,,관리 방법 .혈당 정상 범위임rt foot rolling중으로 pain호소 밤사이 수면양호걱정신경 예민감정변화 중임감정을 표현하도록 지지하고 경청기분상태 condition 조금 나은 듯 하다고 혈당 조절과 관련하여 신경쓰는 모습 보이며 혈당 self로 측정하는 모습 보임혈당 조절에 안내하고 불편감 지속알리도록고혈당 고혈당 의식변화 고혈당 허약감 지남력 혈당조절 안됨고혈당으로 인한 구강 내 감염 위해 식후 양치, gargle 등 구강 위생 격려.당뇨환자의 정기점검 내용과 빈도에 .BST 140 으로 저혈당 호소 밤동안 저혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현기증, 공복감, 두통, 피로감등의 저혈당 에 저혈당 이 있을 즉알려주도록 pain 및 불편감 호소 WA 잘고혈당 고혈당 의식변화 고혈당 허약감 지남력 혈당조절 안됨식사요법, 운동요법, 약물요법을 정확히 지키는 것이 중요을 .저혈당/고혈당 과 대처법에 .혈당정상화, 표준체중의 유지, 정상 혈중지질의 유지에 .고혈당 ,,관리 방법 .혈당측정법,인슐린 자가 투여법, 경구투약,수분 섭취량,대체 탄수화물,의료진의 도움이 필요한 사항에 교혈당 정상 범위임수술부위 oozing Rt.foot rolling keep수술 부위 (출혈, 통증, 부종)수술부위 출혈 상처부위 oozing Wound 당겨지지 않도록 적절한 체위 취하기 설명감염 발생 위험 요인 수술부위 출혈 밤동안 저혈당 호소 수면 양상 양호rt foot rolling유지하며 간호기록지
Word Cloud
Natural Language Processing (NLP)

Heart
SIMENS: CT Cardio-Vascular Engine

ER Multiple Rule-Out Scan of a Patient With Disturbance of Consciousness and Without Breath- Hold.
SOMATOM Definition Flash Scanning
Author: Junichiro Nakagawa, MD,* Isao Ukai, MD*, Osamu Tasaki, MD, PhD*, Tomoko Fujihara**
and Katharina Otani, PhD ***
Date: Dec 13, 2010
* Trauma and Acute Critical Care Center, Osaka University Hospital, Osaka, Japan **CT Marketing Department, Healthcare, Siemens Japan K.K., Tokyo, Japan ***Research & Collaboration Development Department, Healthcare, Siemens Japan K.K., Tokyo, Japan

Brain Vasculature
Color = Vessel Diameters
2011 Optimization of MicroCT Imaging and Blood Vessel Diameter Quantitation of Preclinical Specimen Vasculature with Radiopaque Polymer Injection Medium

fMRI analysis
2013 Sciencce Functional interactions as big data in the human brain

Functional Connectivity
2013 Sciencce Functional interactions as big data in the human brain
N=50,000 voxels
N(N – 1)/2 = 1,249,975,000 Pairs
Seed Region Based Analysis

Obesity fMRI Research Preliminary Pilot Results
p<0.005 uncorrected
Activation of visual, motor and prefrontal brain area in response to visual food cue
3
+
Food presentation max 4sec
Feedback 1sec
Fixation 1~10sec
Choi et al., on going

Resting state fMRI analysis Complex network approach
Parcellation
116 brain regions
by AAL map
Adjacency matrix
Network properties
node degree, clustering coefficient
Choi et al., on going

Bone Quality and TBS (Trabecular Bone Score)

Clinical Implication of TBS
2014 Endocrine. Utility of the trabecular bone score (TBS) in secondary osteoporosis

Overview of secondary data in public health by data source
보험청구자료
건강검진
NHIS (National Health Insurance Corporation): 국민건강보험공단 (보험공단)
HIRA (Health Insurance Review and Assessment Service) :건강보험심사평가원 (심평원)
통계청
암센터
J Korean Med Assoc 2014 May; 근거중심 보건의료의 시행을 위한 빅데이터 활용

Number of patients with medical treatments related to
osteoporosis in each calendar year
2005 2006 2007 2008
0
500
1000
1500
Male
Female
Calendar year
Number (thousand)
2011 JBMM Burden of osteoporosis in adults in Korea- a national health insurance database study

Korean Society for
Bone and Mineral Research
Anti-hypertensive prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
심평원 빅데이터 연구 고혈압약과 골절
Choi et al., in submission

Compare Fracture Risk Comparator?
Hypertension
CCB
High Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007
2011
2008

Distribution of ARB MPR
(Histogram)
ARB Non-user
20
Frequency Density
ARB user
80
120
Medication Possession Ratio (MPR)
Total prescription days
Observation days
350 days (Prescription)
365 days (Observation)
MPR
96%
MPR (%)

NA09-011,10-004_당뇨병_환자에서_심혈관계질환_발생_예방을_위한_아스피린_사용양상_분석

데이터베이스 기반 혈당관리

혈당 변화 실시간 모니터링
저녁 식사전 고혈당

구글 헬스 앱 분야 매출 1위 ‘눔(noom)’

Date
Time
name
foodType
calories
unit
amount
2014-08-09
0
미역국
0
23
1국그릇 (300ml)
105 g
2014-08-09
0
잡곡밥
0
80
1/4공기 (52.5g)
52 g
2014-08-09
0
열무김치
0
3
1/4소접시 (8.75g)
9 g
2014-08-09
0
파프리카
0
6
1/2개 (33.25g)
35 g
2014-08-09
0
토란대무침
0
28
1/2소접시(46.5g)
46 g
2014-08-09
1
복숭아
0
91
1개 (269g)
268 g
2014-08-09
2
마른오징어
2
88
1/4마리 (25g)
25 g
2014-08-09
2
파프리카
0
6
1/2개 (33.25g)
35 g
2014-08-09
2
저지방우유
1
72
1컵 (200ml)
180 g
2014-08-09
2
복숭아
0
183
2개 (538g)
538 g
2014-08-09
3
복숭아
0
91
1개 (269g)
268 g
2014-08-09
3
파프리카
0
6
1/2개 (33.25g)
35 g
2014-08-09
4
파프리카
0
6
1/2개 (33.25g)
35 g
2014-08-09
4
식빵
1
92
1장 (33g)
33 g
2014-08-09
4
삶은옥수수
1
197
1개 반 (150g)
150 g
2014-08-09
4
복숭아
0
91
1개 (269g)
268 g
2014-08-09
4
저지방우유
1
72
1컵 (200ml)
180 g
2014-08-10
0
복숭아
0
91
1개 (269g)
268 g
2014-08-10
0
저지방우유
1
36
1/2컵 (100ml)
90 g
2014-08-10
0
두부
0
20
1/4인분 (25g)
25 g
2014-08-10
0
견과류
2
190
1/4 컵 (50g)
31 g
2014-08-10
0
파프리카
0
11
1개 (66.5g)
65 g
2014-08-10
2
방울토마토
0
8
4개 (60g)
60 g

식사량 실시간 모니터링
0
100
200
300
400
500
600
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
2014-08-09
2014-08-10
2014-08-11
2014-08-12
2014-08-13
2014-08-14
2014-08-15
점심 과식
저녁 금식

운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X

스마트폰 활용 당뇨병 통합관리
의사
상담
교육
조회/ 분석
교육간호사
매주/필요시
진료
처방
2-3달 간격
평가 회의
매주/필요시
식사/ 운동
스마트폰
데이터베이스 서버
자가관리
전송
분석
혈당 측정
혈당측정기

Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted Cancer Treatment (EGFR)
Causal Variant Targeted Drug (MODY-SU)
Drug Efficacy/Side Effect Related Genotype (CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular Classification
-Prognosis (Leukemia)
Hereditary Cancer (BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction (Complex disease, Diabetes)
Germline Variants

저의 유전자 분석 결과를 반영하여 진료해주세요!!
헠?

Cancer Targeted Therapy
Targeted Therapy
Genetic Test
Cancer

Disease Risk and Pharmacogenomics
2012 European Heart Journal. Personalized medicine: hope or hype?

Promise of Human Genome Project

Predicting Complex Diseases
2013 NG Predicting the influence of common variants
“For most diseases, it should be possible to identify the individuals with the highest genetic risk. However, if the aim is to identify individuals with just twice the mean population risk, we cannot currently do that with SNPs”
Mean population risk
Highest genetic risk
x2
Disease Risk
Rare Variant?

Variants and Disease Susceptibility
2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges

Genotype Based Diabetes Therapy
Diabetes due to KATP channel mutations  sulphonylurea
2007 American Journal of Physiology - Endocrinology and Metabolism. ATP-sensitive K+ channels and disease- from molecule to malady

Influence of Genetics on Human Disease
For any condition the overall balance of genetic and environmental determinants can be represented by a point somewhere within the triangle.
105
Single Locus / Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer

2008 HMG Genome-based prediction of common diseases- advances and prospects

Diabetes ≠ Genetic Disease?
•Familial aggregation
–Genetic influences?
–Epigenetic influences
•Intrauterine environment
–Shared family environment?
•Socioeconomic status
•Dietary preferences
•Food availability
•Gut microbiome content
•Overestimated heritability
–Phantom heritability
2012. Drong AW, Lindgren CM, McCarthy MI. Clin Pharmacol Ther. The genetic and epigenetic basis of type 2 diabetes and obesity.
2012. PNAS The mystery of missing heritability- Genetic interactions create phantom heritability

2013.01.29
 2014.7. 18개 대형병원

Influence of Genetics on Human Disease
For any condition the overall balance of genetic and environmental determinants can be represented by a point somewhere within the triangle.
113
Single Locus / Mendelian
Multiple Loci or multi- chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet Carcinogens Infections Stress Radiation Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer

Mendelian (single-gene) genetic disorder
Known single-gene candidates testing
Whole Genome or Whole Exome Sequencing

2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec

2013.11.14. 141 drugs 158 labels 60 indications/contraindications Atorvastatin, Azathioprine, Carbamazepine, Carvediolol, Clopidogrel, Codein, Diazepam…..

CPIC Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 biomarkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation
Clinical Interpretation
1
Clopidogrel
CYP2C19
rs4244285 (G>A)
GG
*1/*1
(EM)
Use standard dose
rs4986893 (G>A)
GG
rs12248560 (C>T)
CC
2
Warfarin
VKORC1
rs9923231 (C>T)
TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T)
CC
rs1057910 (A>C)
AC
3
Simvastatin
SLCO1B1
rs4149056 (T>C)
TT
Normal
4
Azathioprine (AP), MP, or TG
TPMT
rs1142345 (A>G)
AA
Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T)
CT
Normal
rs3909184 (C>G)
CC
6
Abacavir
HLA-B*5701
rs2395029 (T>G)
TT
Normal
7
Allopurinol
HLA-B*5801
rs9263726 (G>A)
GG
Normal
Clopidogrel1): UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor) Warfarin2): high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
$99 N>400,000

Future of Genomic Medicine?
Test when needed
Without information
Know your type
Blood type
Geno type
Here is my sequence

Genome Big Data + Brain Big Data

Voxel-wise GWAS
2014 Nature Neuroscience. Whole-genome analyses of whole-brain data- working within an expanded search space

Connectome-wide GWAS

Disease GWAS vs. Whole-brain GWAS

511,089 SNPs Illumina Beadchip
Functional MRI
N = 1,620

2014 PLOS GENT. Global genetic variations predict brain response to faces

2014 PLOS GENT. Global genetic variations predict brain response to faces
Connectivity of face network

2014 JAMA Finding the Missing Link for Big Biomedical Data

Contents
1.What is Healthcare Big Data?
2.Healthcare Big Data
①Electrical Health Records (EHR)
Structured/Unstructured Data
②Medical Images
③Government Data
④Behavior/Sensor Data
⑤Genetic Data
3.Clinical and Research Applications

Clinical and Research Applications
1.Clinical Decision Support
2.Public Health Surveillance
3.Personal Health Record
4.Healthcare Data Integration

Medical Big Data  Artificial Intelligence

Jeopardy!
2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지

Social Network and Obesity Prevalence
2013 PLOS One. Assessing the Online Social Environment for Surveillance of Obesity Prevalence

2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza
Google Flu Trends

2014 Science. Big data. The parable of Google Flu- traps in big data analysis

Big data platform model by Korea Institute of Drug Safety and Risk Management

Pros and Cons of Different Data Sources
2014 ASBMR meet the professor handbook

insurance claims, blood tests, medical histories
US$10-million pilot study N=30M

http://hineca.tistory.com/entry/Vol11-3%EC%9B%94%ED%98%B8-%EB%B3%B4%EA%B1%B4%EC%9D%98%EB%A3%8C%EC%9D%B4%EC%8A%88- %EA%B7%BC%EC%8B%9C%EA%B5%90%EC%A0%95%EC%88%A0

Medical Open Innovation Connectivity (MOIC)

Clinical and Research Applications
1.Clinical Decision Support
2.Public Health Surveillance
3.Personal Health Record
4.Healthcare Data Integration
5.How to Prepare for Big Data Driven Healthcare

빅데이터 연구 적용
전통적인 관점 연구
Large scale
(unstructured)
data
Summary
(Modify)
Classical hypothesis driven study
새로운 관점 연구
Hypothesis Generating Study

임상의사 관점의 의료빅데이터 연구와 임상적용 - From Clinic To Data, From Data To Clinic

임상의사 관점의 의료빅데이터 연구와 임상적용 - From Clinic To Data, From Data To Clinic

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