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

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

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

  1. 1. 1
  2. 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. 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. 4. $215MPrecision Medicine Initiative 2015/1/30
  5. 5. Integration of Multi-Big-Data
  6. 6. 7
  7. 7. Hypothesis Driven Science Data Driven Science Hypothesis Collect Data Data Generate Hypothesis Analyze Analyze
  8. 8. Candidate Gene Approach Genome-wide Approach Choose a Gene from Prior Knowledge Analyze the Gene Analyze ALL Genes Discover Novel Findings
  9. 9. GWAS (Genome wide association study) SNP chip Whole Genome SNP Profiling (500K~1M SNPs) Common Variant Choi HJ, Doctoral Thesis
  10. 10. Estrada et al., Nature Genetics, 2012 + novel targets for bone biology Recent largest GWAS GEFOS consortium
  11. 11. 2010 An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus Environment-Wide Association Study (EWAS)  다양한 환경인자들 
  12. 12. 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
  13. 13. GWAS  PheWAS Phenotype-wide Association Study
  14. 14. 1000 개의 질병들 Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Phenotype-wide Association Study
  15. 15. Genome-Envirome-Phenome-wide Association Study Phenome-wide (Lab,Diagnosis) Proposal (Choi) Genome-wide Environment-wide (Life style, diet, exercise, pollution)
  16. 16. Anatome-Phenome-wide Association Study 2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants Phenome Anatome
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. Cancer Targeted Therapy 21 Targeted TherapyGenetic TestCancer Somatic Mutation
  21. 21. 22
  22. 22. 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
  23. 23. 2008 HMG Genome-based prediction of common diseases- advances and prospects24
  24. 24. 2008 HMG Genome-based prediction of common diseases- advances and prospects25
  25. 25. 26
  26. 26. 27
  27. 27. Genetics of eating behavior 2011 Genetics of eating behavior
  28. 28. Mendelian (single-gene) genetic disorder Known single-gene candidates testing Whole Genome or Whole Exome Sequencing 30
  29. 29. Laboratory Director 강현석 31
  30. 30. 32
  31. 31. 2012 European Heart Journal. Personalized medicine: hope or hype? Herceptin Glivec 33
  32. 32. 34 Pharmacogenomic Biomarkers in Drug Labeling (N=166) 2015.9.14. Atorvastatin, Azathioprine, Carbamazepine, Carvediolol, Clopidogrel, Codein, Diazepam…..
  33. 33. 30만원-200만원 35
  34. 34. 15만원 DTC (Direct to Consumer) 테라젠 진스타일 테라젠 홈페이지 http://hellogene.com/genestyle_
  35. 35. DTC (Direct to Consumer) 테라젠 진스타일 테라젠 홈페이지 http://hellogene.com/genestyle_
  36. 36. 38 테라젠 홈페이지 http://hellogene.com/genestyle_
  37. 37. 39
  38. 38. (출처: 금창원 대표님 블로그) $99 TV CF 40
  39. 39. 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 ?
  40. 40. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing
  41. 41. 43 [출처: 중앙일보] “신장암·전립선암 조심하세요” 3만여 개 내 유전자가 경고했다 2017.3.15.
  42. 42. Future of Genomic Medicine? Test when neededWithout information Know your type Blood type Geno type Here is my sequence
  43. 43. 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
  44. 44. Electronic Health Records 2012 NRG Mining electronic health records- towards better research applications and clinical care 46
  45. 45. 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)
  46. 46. 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
  47. 47. PCA Analysis혈당 신장기능 51
  48. 48. Machine Learning 2014 Big data bioinformatics52
  49. 49. Clinical Notes 53
  50. 50. 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
  51. 51. 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
  52. 52. 58
  53. 53. 59
  54. 54. 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
  55. 55. 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
  56. 56. 62
  57. 57. 63
  58. 58. 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
  59. 59. Big data platform model by Korea Institute of Drug Safety and Risk Management
  60. 60. 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
  61. 61. 2014 Radiomics 2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
  62. 62. 68
  63. 63. 69 2017 Scientific Reports. Precision Radiology- Predicting longevity using feature engineering and deep learning methods in a radiomics framework
  64. 64. 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
  65. 65. 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
  66. 66. 72
  67. 67. 73
  68. 68. ‘닥터 인공지능’, 의사가 놓친 결핵을 찾아내다 74 2016.8.8.
  69. 69. 75
  70. 70. 뷰노의 골연령 판독 76 카카오AI리포트 : 정규환: AI 의료영상 기술 활용 사례 2017.7.28.
  71. 71. 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
  72. 72. 78 2015 Sci Transl Med. The emerging field of mobile health
  73. 73. 피부 사진 원격 진단/처방 80
  74. 74. 81
  75. 75. 심전도 원격 진단/처방 82
  76. 76. Weight Gain over the Holidays 83 2016 NEJM Weight Gain over the Holidays in Three Countries
  77. 77. 84
  78. 78. 85http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=report_s
  79. 79. 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
  80. 80. 데이터베이스 기반 혈당관리 87
  81. 81. 88
  82. 82. 혈당 변화 실시간 모니터링 저녁 식사전 고혈당 89
  83. 83. 90
  84. 84. 91
  85. 85. 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
  86. 86. 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
  87. 87. 운동량 실시간 모니터링 0 5000 10000 15000 20000 25000 걸음 수 운동X 94
  88. 88. 95
  89. 89. Evolution of Diabetes Control Insulin Glucose Insulin Injection Episodic Glucose Monitoring Continuous Insulin Injection Continuous Glucose Monitoring Artificial Pancreas
  90. 90. Blood Glucose Sensor and Insulin Pump for Diabetes 97
  91. 91. 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
  92. 92. 2014 JAMA Finding the Missing Link for Big Biomedical Data
  93. 93. 100
  94. 94. Apple Health App
  95. 95. 2015 Nature Immunology. A vision and a prescription for big data–enabled medicine Big Data–Enabled Medicine
  96. 96. 104 카카오AI리포트: 안성민: 데이터 기반 정밀 의료와 AI 2017.7.28.
  97. 97. 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
  98. 98. Healthcare Big Data + Artificial Intelligence 106 Healthcare Big Data Machine Learning Novel Insights and Applications
  99. 99. 107 Humans Need Not Apply / 사람은 필요 없습니다.
  100. 100. 108
  101. 101. 109
  102. 102. 110
  103. 103. 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.
  104. 104. 112 December 13, 2016
  105. 105. 113
  106. 106. 인공지능 진단 민감도/특이도 114
  107. 107. Disease Classes Clustering 115
  108. 108. 116 피부과 전문의 한승석
  109. 109. 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;
  110. 110. 118 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  111. 111. 119 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  112. 112. 120 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  113. 113. 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
  114. 114. 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
  115. 115. 123
  116. 116. 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
  117. 117. 대사질환 정밀의학 구현 Environment Gene Eat Exercise Brain Cognitive Hormone Behavior 산자부 과제 지원 예정
  118. 118. Multidimensional Architecture 126 Gene Hormone Psychology Behavior Phenotype Outcome Metabolic Disease Vascular Disease Environment
  119. 119. 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
  120. 120. Environment Survey NeuroimagingGenetics Lab/Hormone Hospital Cognitive Personalize Psychotherapy Dietary Intervention Exercise Intervention Food Exercise Glucose Mobile Drug Neuromodulation Monitoring

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