정밀의료와 다차원 의료데이터(유전자, Ehr, 국가자료, 영상, 센서-웨어러블)

1. 1

2. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
2

3. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
3

4. $215MPrecision Medicine Initiative
2015/1/30

5. Integration of Multi-Big-Data

7. 7

8. Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

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

10. GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant
Choi HJ, Doctoral Thesis

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

12. 2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

13. Nutrient-wide
association study
13 2016 AJCN Nutrient-wide association study of 57 foods-nutrients and epithelial ovarian cancer in the European Prospective Investigation into Cancer and Nutrition

14. GWAS  PheWAS
Phenotype-wide Association Study

15. 1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

16. Genome-Envirome-Phenome-wide
Association Study
Phenome-wide
(Lab,Diagnosis)
Proposal
(Choi)
Genome-wide
Environment-wide
(Life style, diet, exercise, pollution)

17. Anatome-Phenome-wide Association Study
2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants
Phenome
Anatome

18. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
18

19. Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(Tratis=2000)
Disease-related variations
Functional elements
2017-07-31
19

20. 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
Fetal DNA

21. Cancer Targeted Therapy
21
Targeted TherapyGenetic TestCancer
Somatic
Mutation

22. 22

23. Influence of Genetics on Human Disease
For any condition the overall balance of g
enetic and environmental determinants ca
n be represented by a point somewhere w
ithin the triangle.
23
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

24. 2008 HMG Genome-based prediction of common diseases- advances and prospects24

25. 2008 HMG Genome-based prediction of common diseases- advances and prospects25

26. 26

27. 27

28. Genetics of eating behavior
2011 Genetics of eating behavior

30. Mendelian (single-gene) genetic
disorder
Known single-gene
candidates testing
Whole Genome or Whole
Exome Sequencing
30

31. Laboratory Director
강현석
31

32. 32

33. 2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
33

34. 34
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..

35. 30만원-200만원
35

36. 15만원
DTC (Direct to Consumer)
테라젠 진스타일
테라젠 홈페이지 http://hellogene.com/genestyle_

37. DTC (Direct to Consumer)
테라젠 진스타일
테라젠 홈페이지 http://hellogene.com/genestyle_

38. 38 테라젠 홈페이지 http://hellogene.com/genestyle_

39. 39

40. (출처: 금창원 대표님 블로그)
$99
TV CF
40

41. Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (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
=0
최형진
+1,000,000
?

42. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

43. 43
[출처: 중앙일보] “신장암·전립선암 조심하세요” 3만여 개 내 유전자가 경고했다 2017.3.15.

44. Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence

45. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
45

46. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
46

47. 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)

50. 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

51. PCA Analysis혈당
신장기능
51

52. Machine Learning
2014 Big data bioinformatics52

53. Clinical Notes
53

54. 밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 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, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)54

57. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
57

58. 58

59. 59

60. 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., 2015 International Journal of Cardiology

61. 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 20112008
Choi et al., 2015 International Journal of Cardiology61

62. 62

63. 63

64. 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

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

66. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
66

67. 2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

68. 68

69. 69 2017 Scientific Reports. Precision Radiology- Predicting longevity using feature engineering and deep learning methods in a radiomics framework

70. 사망률 높을 것으로 예측되는 영상(왼쪽)과
낮을 것으로 예측되는 영상(오른쪽)
70 2017 Scientific Reports. Precision Radiology- Predicting longevity using feature engineering and deep learning methods in a radiomics framework

71. Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images

72. 72

73. 73

74. ‘닥터 인공지능’,
의사가 놓친 결핵을 찾아내다
74
2016.8.8.

75. 75

76. 뷰노의 골연령 판독
76
카카오AI리포트 : 정규환: AI 의료영상 기술 활용 사례 2017.7.28.

77. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
77

78. 78
2015 Sci Transl Med. The emerging field of mobile health

80. 피부 사진 원격 진단/처방
80

81. 81

82. 심전도 원격 진단/처방
82

83. Weight Gain over the Holidays
83 2016 NEJM Weight Gain over the Holidays in Three Countries

84. 84

85. 85http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=report_s

86. 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

87. 데이터베이스 기반 혈당관리
87

88. 88

89. 혈당 변화 실시간 모니터링
저녁 식사전 고혈당
89

90. 90

91. 91

92. 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
92

93. 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
93

94. 운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X
94

95. 95

96. Evolution of Diabetes Control
Insulin
Glucose
Insulin
Injection
Episodic
Glucose
Monitoring
Continuous
Insulin
Injection
Continuous
Glucose
Monitoring
Artificial
Pancreas

97. Blood Glucose Sensor and
Insulin Pump for Diabetes
97

98. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
98

99. 2014 JAMA Finding the Missing Link for Big Biomedical Data

100. 100

101. Apple Health App

103. 2015 Nature Immunology. A vision and a prescription for big data–enabled medicine
Big Data–Enabled Medicine

104. 104
카카오AI리포트: 안성민: 데이터 기반 정밀 의료와 AI 2017.7.28.

105. Contents
1. What is Data-Driven Healthcare?
2. Healthcare Big Data
① Genetic Data
② Electrical Health Records
③ National Healthcare Data
④ Medical Images
⑤ Sensor/Mobile Data
⑥ Data Integration
3. Healthcare Big Data + Artificial Intelligence
105

106. Healthcare Big Data
+ Artificial Intelligence
106
Healthcare Big Data Machine Learning
Novel Insights and Applications

107. 107 Humans Need Not Apply / 사람은 필요 없습니다.

108. 108

109. 109

110. 110

111. 111
왓슨 도입 후 달라진 점 중 하나는 환자를 대하는
의료진의 긴장도가 높아졌다는 것. 길병원은 의
료진이 왓슨과 다른 치료법을 추천할 땐 분명한
근거를 제시하도록 했다.
1월엔 부산대병원이, 이달 16일엔 건양대병원이
길병원에 이어 왓슨을 도입했다. 현재 왓슨은 결
장·직장, 유방, 폐, 위, 자궁암만을 진단할 수 있지
만 4월엔 난소암이 추가되고 연말엔 방광, 전립
샘, 혈액암(백혈병)까지 추가돼 암종 85%를 커버
할 수 있게 된다.

112. 112
December 13, 2016

113. 113

114. 인공지능 진단 민감도/특이도
114

115. Disease Classes Clustering
115

116. 116
피부과 전문의 한승석

117. Personalized Nutrition by
Prediction of Glycemic Responses
117
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5, 2015;
Received in revised form:
October 29, 2015;
Accepted: October 30, 2015;

118. 118
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

119. 119
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

120. 120
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

121. Google spin-off deploys wearable
electronics for huge health study
121
4년간 10,000명 - 건강 상태 데이터 축적
심박수, 수면패턴, 유전 정보, 감정 상태, 진료기록, 가족력, 소변/타액/혈액 검사
스탠퍼드 대학병원/듀크 대학병원
https://www.nature.com/articles/d41586-017-01144-1

122. Zuckerberg, Chan give UCSF $10
million for health data research
122
http://www.sfchronicle.com/business/article/Zuckerberg-Chan-give-UCSF-10-million-for-health-11534088.php

123. 123

124. Psychology
Behavior
Physical
Biology
Digital
Cognition
Mood
Motivation
Food
Exercise
Noom
Inbody
Food Diary
Pedometer
Impedance
Weight
Body size:
WC
Body
Composition
Fat mass
Genetics Hormone
Brain Activity
fMRI
Metabolites
Blood
Glucose
Energy
Expenditure
Indirect
Calorimetry
Buffet
Daily Intake (recall)
CBT
CT BT
Therapy
Q:
YFAS,
BES,
DEBQ
Drug
Food Diary
Pedometer
Neuromodulation
Logic
?
Therapist
Blood
Sampling

125. 대사질환 정밀의학 구현
Environment
Gene
Eat
Exercise
Brain
Cognitive
Hormone
Behavior
산자부 과제 지원 예정

126. Multidimensional Architecture
126
Gene
Hormone
Psychology
Behavior
Phenotype
Outcome
Metabolic
Disease
Vascular
Disease
Environment

127. 127
Environment
Gene
Eat
Exercise
Metabolism
Brain
Glucose
DM
Blood Pressure
HTN
Cardiovascular
Disease
Cognitive
Hormone
Behavior
Psychotherapy
Behavior Therapy
Policy Making
Genetic Testing
Neuroimaging
Neuromodulation
Drug
Drug
Lab
Survey
Survey
Sensor
DrugDiagnosis
EMR
Government
Data
Mining

128. Environment Survey
NeuroimagingGenetics Lab/Hormone Hospital
Cognitive
Personalize
Psychotherapy
Dietary
Intervention
Exercise
Intervention
Food
Exercise
Glucose
Mobile
Drug
Neuromodulation
Monitoring