의료 빅데이터와 인공지능의 현재와 미래

1. 1

2. Contents
1. What is Healthcare Big Data?
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 Healthcare Big Data?
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

6. Integration of Multi-Big-Data

8. 8

9. 미국 내분비 학회
9

10. 미국 골대사 학회
10

11. 11

12. 12

13. 13

14. 14

15. Big Data Dimensions
New Analysis Tool (R, Python, Matlab)
New Approach15

16. 16

17. Data Variety
17

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

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

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

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

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

23. 2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

24. GWAS  PheWAS
Phenotype-wide Association Study

25. 1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

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

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

28. 의료 빅데이터의 새로운 역할
전통적인 관점 연구
Large scale
(unstructured)
data
Summary
(Modify)
Classical hypothesis driven study
새로운 관점 연구
Hypothesis Generating Study

29. Contents
1. What is Healthcare Big Data?
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
29

30. 저의 유전자
분석 결과를
반영하여 진료
해주세요!! 헠?
30

31. DNA
mRNA
Protein
Metabolite
Epigenetics
Genetics Information and OMICs
Genomics
Epigenomics
Transcriptomics
Proteomics
Metabolomics
31

32. DNA
mRNA
WBC Liver Cancer
Germline mutation Somatic mutation
Epigenomics
Proteomics
Metabolomics
Epigenomics
Proteomics
Metabolomics
Epigenomics
Proteomics
Metabolomics
Metabolomics
Germline
Genomics
Cancer
Genomics
Transcriptomics Transcriptomics Transcriptomics
Proteomics
32

33. Promise of Human Genome Project

34. 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
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
34

35. All Major Tissues/Organs
All Proteins + All RNAs
2015 Science Tissue-based map of the human proteome
1. Immunohistochemistry (IHC)
24,028 antibodies
(16,975 proteins)
 >13 million IHC images
2. RNA-sequencing
(N=44)
35

36. 111 Reference Human Epigenomes
2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes36

37. 37

38. Data Dimensions
2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes38

39. Building the Interactome
2015 Science Uncovering disease-disease relationships through the incomplete interactome39

40. Network-based Model of
Disease-disease Relationship
2015 Science Uncovering disease-disease relationships through the incomplete interactome40

41. 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

42. 1) Cancer Genome
42

43. Cancer Targeted Therapy
43
Targeted TherapyGenetic TestCancer

44. 44

45. Cancer Rebiopsy
2013 JCO Genomics-Driven Oncology- Framework for an Emerging Paradigm

46. Liquid Biopsy
46

47. 2) Common Complex Disease
Susceptibility
47

48. 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.
48
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

49. 2008 HMG Genome-based prediction of common diseases- advances and prospects49

50. 2008 HMG Genome-based prediction of common diseases- advances and prospects50

52. 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 heritability52

53. 53

54. 54

55. 55

56. Genetics of eating behavior
2011 Genetics of eating behavior

57. Gene-Environment Interaction
Gene Environment
Disease
Genetic Predisposition Score Sugar-Sweetened Beverages

58. Soda School
No-Soda School
Obese Family Lean Family

59. 3) Rare Mendelian Disease
Susceptibility
59

61. Mendelian (single-gene) genetic
disorder
Known single-gene
candidates testing
Whole Genome or Whole
Exome Sequencing
61

62. Laboratory Director
강현석
62

63. 63

64. 4) Pharmacogenomics
64

65. 2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
65

66. 66
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..

67. Large Effect Size Variant?
Disease susceptibility variant Pharmacogenetic variant
Environmental
Exposure
Drug
Exposure

68. 5) Other Genome
Fetal DNA
Microbiome (Bacteria, Virus DNA)
68

69. 69

71. 30만원-200만원
71

72. 72

73. (출처: 금창원 대표님 블로그)
$99
TV CF
73

74. 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
?

75. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

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

77. Contents
1. What is Healthcare Big Data?
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. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
78

79. 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)

80. Medication Data

81. Lab Data

84. 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

85. PCA Analysis혈당
신장기능
85

86. Machine Learning
2014 Big data bioinformatics86

87. Clinical Notes
87

88. 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)88

91. 91

92. Contents
1. What is Healthcare Big Data?
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
92

93. 93

94. 94

96. 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

97. 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

98. 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 Cardiology98

99. 99

100. Distribution of ARB MPR
(Histogram)
ARB Non-user
20
FrequencyDensity
ARB user
80 120
Medication Possession Ratio (MPR)
Total prescription days
Observation days
350 days (Prescription)
365 days (Observation)
MPR
96%
MPR (%)
Choi et al., 2015 International Journal of Cardiology100

101. Data Variety
101

102. 102

104. 104

105. 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

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

107. Contents
1. What is Healthcare Big Data?
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
107

108. Heart
SIMENS: CT Cardio-Vascular Engine

109. 2013 Science Structural and Functional Brain Networks- From Connections to Cognition

111. fMRI analysis
2013 Science Functional interactions as big data in the human brain

112. 2013 Science Functional interactions as big data in the human brain

113. 113
2013 Science Functional interactions as big data in the human brain
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns

114. 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 going114

115. 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

116. Genome Big Data + Brain Big Data
116

117. Connectome-wide GWAS
2014 Nature Neuroscience. Whole-genome analyses of whole-brain data- working within an expanded search space117

118. 2015 Nature. Common genetic variants influence human subcortical brain structures
Published
online
21 January
2015
118

119. 2015 Nature. Common genetic variants influence human subcortical brain structures119

120. 2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

121. 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

122. 122

123. 123

124. 124

125. 딥러닝으로 폐암 진단을 돕는다,
의사를 위한 인공 지능
‘뷰노 메드(Vuno-Med)’
125
Artificial Intelligence for Doctors and Patients

126. 126

127. Contents
1. What is Healthcare Big Data?
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
127

128. 128
2015 Sci Transl Med. The emerging field of mobile health

130. 130

131. 131

132. 피부 사진 원격 진단/처방
132

133. 133

134. 심전도 원격 진단/처방
134

135. NFC 혈당측정기
135

136. 데이터베이스 기반 혈당관리
136

137. 137

138. 혈당 변화 실시간 모니터링
저녁 식사전 고혈당
138

139. 139

140. 140

141. 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
141

142. 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
142

143. 운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X
143

144. 144

145. 운동
식사
145

146. 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

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

149. 2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza
Google Flu Trends

150. 150

151. 151http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=report_s

152. 152 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits
November 3, 2015

153. 153 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits

154. Personalized Nutrition by
Prediction of Glycemic Responses
154
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5, 2015;
Received in revised form:
October 29, 2015;
Accepted: October 30, 2015;

155. 155
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

156. 156
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

157. 157
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

158. Contents
1. What is Healthcare Big Data?
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
158

159. 159

160. 2014 JAMA Finding the Missing Link for Big Biomedical Data

161. electronic me
dical records (
EMRs) and g
enotype data
from 11,210 i
ndividuals
2015 STM Identification of type 2 diabetes subgroups through topological analysis of patient similarity

162. 2015 Sci Rep. Repurpose
terbutaline sulfate for
amyotrophic lateral sclerosis
using electronic medical records

163. 2015 Sci Rep. Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records

164. 164

165. Apple Health App

167. 2015 Nature Immunology. A vision and a prescription for big data–enabled medicine

168. 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
2014.3.

169. 169
국가기관
의료기관
연구기관
개인
개인식별
정보통합
정보저장
통계
인공지능
건강관리
정보공개
수집 분석 활용
개인
집단
Overview of Healthcare Data

170. Contents
1. What is Healthcare Big Data?
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
170

171. Healthcare Big Data
+ Artificial Intelligence
171
Healthcare Big Data Machine Learning
Novel Insights and Applications

172. 172

175. 175

176. 176
In a scan of 3,000 images, IBM
technology was able to spot
melanoma with an accuracy
of about 95 percent, much
better than the 75 percent to
84 percent average of today's
largely manual methods.
IBM Research will continue to
work with Sloan Kettering to
develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014

177. 177
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other signs of
diseases.
In the long run, IBM and others in the field hope such
systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the world
where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded 50
million electronic medical records.

178. 178

179. 179
Dec. 10, 2015
Novo Nordisk A/S is
teaming up with IBM
Watson Health, a
division of International
Business Machines
Corp., to create a
“virtual doctor” for
diabetes patients that
could dispense
treatment advice such
as insulin dosage.

180. 180
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

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

182. Multidimensional Architecture
182
Gene
Hormone
Psychology
Behavior
Phenotype
Outcome
Metabolic
Disease
Vascular
Disease
Environment

183. The FUTURE MEDICINE
is already
at PRESENT
183