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(서울의대 공유용) 빅데이터 분석 유전체 정보와 개인라이프로그 정보 활용-2015_11_24

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(서울의대 공유용) 빅데이터 분석 유전체 정보와 개인라이프로그 정보 활용-2015_11_24

  1. 1. 빅데이터 분석: 유전체 정보와 개인라이프로그 정보 활용 서울대 최형진1
  2. 2. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  3. 3. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  4. 4. $215MPrecision Medicine Initiative 2015/1/30
  5. 5. Integration of Multi-Big-Data
  6. 6. 8
  7. 7. 2013.4.25. KBS 9시 뉴스
  8. 8. 며칠 전 유전자 검사를 받은 40대 남성입니다. 혈액세포의 DNA 상태를 분석해 앞으로 암에 걸릴 위험이 있는지 여부를 판단할 수 있다 고 합니다. 2013.4.25. KBS 9시 뉴스
  9. 9. 2013.4.25. KBS 9시 뉴스 60년전 DNA의 구조가 밝혀진 이래 2003년 인간 유전자 지도가 완성됐고, 현재는 어떤 유 전자가 어떤 질병을 일으키는지 분석도 80% 정도 끝난 상태입니다. 예를들어 13번 염색체의 BRCA2 유전자에 이상이 생기면 유방암에 걸릴 확률이 높습니 다. 또 17번 염색체 유전자는 난소암, 7번 염색체 유전자는 비만을 일으킵니다.
  10. 10. 1997
  11. 11. 13 Heart Disorder 99% Probability Life Expectancy 30.2 Years
  12. 12. 14
  13. 13. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  14. 14. Promise of Human Genome Project
  15. 15. 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 17
  16. 16. 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) 18
  17. 17. 111 Reference Human Epigenomes 2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes19
  18. 18. 20
  19. 19. Data Dimensions 2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes21
  20. 20. Network-based Model of Disease-disease Relationship 2015 Science Uncovering disease-disease relationships through the incomplete interactome22
  21. 21. Hypothesis Driven Science Data Driven Science Hypothesis Collect Data Data Generate Hypothesis Analyze Analyze
  22. 22. Candidate Gene Approach Genome-wide Approach Choose a Gene from Prior Knowledge Analyze the Gene Analyze ALL Genes Discover Novel Findings
  23. 23. GWAS (Genome wide association study) SNP chip Whole Genome SNP Profiling (500K~1M SNPs) Common Variant Choi HJ, Doctoral Thesis
  24. 24. Estrada et al., Nature Genetics, 2012 + novel targets for bone biology Recent largest GWAS GEFOS consortium
  25. 25. 2010 An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus Environment-Wide Association Study (EWAS)  다양한 환경인자들 
  26. 26. GWAS  PheWAS Phenotype-wide Association Study
  27. 27. 1000 개의 질병들 Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Phenotype-wide Association Study
  28. 28. Genome-Envirome-Phenome-wide Association Study Phenome-wide (Lab,Diagnosis) Proposal (Choi) Genome-wide Environment-wide (Life style, diet, exercise, pollution)
  29. 29. Anatome-Phenome-wide Association Study 2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants Phenome Anatome
  30. 30. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  31. 31. 저의 유전자 분석 결과를 반영하여 진료 해주세요!! 헠? 33
  32. 32. What is Genomic Medicine?
  33. 33. 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
  34. 34. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  35. 35. Cancer Targeted Therapy 37 Targeted TherapyGenetic TestCancer
  36. 36. 38
  37. 37. Cancer Rebiopsy 2013 JCO Genomics-Driven Oncology- Framework for an Emerging Paradigm
  38. 38. Liquid Biopsy 40
  39. 39. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  40. 40. (출처: 금창원 대표님 블로그) $99 TV CF 42
  41. 41. 43
  42. 42. 30만원-200만원 44
  43. 43. GWAS N=339,224 individuals 97 BMI-associated loci
  44. 44. Tissue Specific Gene Expression 2015 Nature Genetic studies of body mass index yield new insights for obesity biology - Hypothalamus, Pituitary gland (appetite regulation) - Hippocampus, Limbic system (learning, cognition, emotion, memory)
  45. 45. Gene Set Enrichment Analysis 2015 Nature Genetic studies of body mass index yield new insights for obesity biology
  46. 46. Per-allele effect of BMI-associated loci on body weight 2012 Genetic determinants of common obesity and their value in prediction
  47. 47. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic
  48. 48. 2015 Nature Genetic studies of body mass index yield new insights for obesity biology Blue: Previous Red: Novel
  49. 49. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic TCF7L2
  50. 50. ◇◆ ‘parental obesity’ as a test to predict obesity in adult life •Dark blue 1–2 yrs •Green 3–5 yrs •Red 6–9 yrs •Light blue 10–14 yrs •Grey , 15–17 yrs Genetic Prediction of Obesity Risk The predictive ability of the currently established BMI- associated loci is poor 2012 Genetic determinants of common obesity and their value in prediction
  51. 51. 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. 53 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
  52. 52. 2008 HMG Genome-based prediction of common diseases- advances and prospects54
  53. 53. 2008 HMG Genome-based prediction of common diseases- advances and prospects55
  54. 54. 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 heritability57
  55. 55. Rare Variant in a Specific Population • 3756 Latino: whole exome sequencing  A rare functional variant in candidate gene  14276: replication  Not found in other ethnic group 2014 JAMA Association of a Low-Frequency Variant in HNF1A With Type 2 Diabetes in a Latino Population
  56. 56. 2014 NEJM Null Mutation in Hormone-Sensitive Lipase Gene and Risk of Type 2 Diabetes Rare Functional Variant = Monogenic Heritable Disease All Amish
  57. 57. Variants and Disease Susceptibility 2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges
  58. 58. 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
  59. 59. Mendelian (single-gene) genetic disorder Known single-gene candidates testing Whole Genome or Whole Exome Sequencing 64
  60. 60. Laboratory Director 강현석 65
  61. 61. 66
  62. 62. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  63. 63. 2012 European Heart Journal. Personalized medicine: hope or hype? Herceptin Glivec 68
  64. 64. 69 Pharmacogenomic Biomarkers in Drug Labeling (N=166) 2015.9.14. Atorvastatin, Azathioprine, Carbamazepine, Carvediolol, Clopidogrel, Codein, Diazepam…..
  65. 65. Large Effect Size Variant? Disease susceptibility variant Pharmacogenetic variant Environmental Exposure Drug Exposure
  66. 66. November 19, 2013
  67. 67. November 19, 2013
  68. 68. 2013 NEJM A Randomized Trial of Genotype-Guided Dosing of Warfarin
  69. 69. 2013 NEJM A Randomized Trial of Genotype-Guided Dosing of Warfarin Median 21 days Median 29 days Median 44 days Median 59 days P<0.001 P=0.003
  70. 70. Expected Metformin response Other drug Metformin usual dose Metformin low dose (S/E) 0% -1% -2%-1.5% -2.5% -3%+0.5% HbA1c change Good Response Genotype Poor Response Genotype
  71. 71. 77
  72. 72. 78
  73. 73. 79
  74. 74. 80
  75. 75. 81
  76. 76. Genetics of eating behavior 2011 Genetics of eating behavior
  77. 77. Personalized Medicine Pharmacogenomics Nutrigenomics IRS1 SNP GA/AA High fat/ Low carb IRS1 SNP GG Standard Higher effect Similar effect 2013 Diabetes Care. IRS1 Genotype Modulates Metabolic Syndrome Reversion in Response to 2-Year Weight-Loss Diet Intervention - The POUNDS LOST trial
  78. 78. Gene-Environment Interaction Gene Environment Disease Genetic Predisposition Score Sugar-Sweetened Beverages
  79. 79. Soda School No-Soda School Obese Family Lean Family
  80. 80. Genotype Guided Personalized Treatment Baseline Genotyping - Drug metabolism - DM etiology - DM complication 1 week 3 month Long term Genotype based treatment strategy - Drug choice - Drug dose - Lifestyle modification - Complication evaluation New T2DM
  81. 81. 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 ?
  82. 82. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  83. 83. 89
  84. 84. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing
  85. 85. A Genotype-First Approach to Defining the Subtypes of a Complex Disease 2014.2.27.
  86. 86. Point-of-care Genotyping HyBeacon Probes
  87. 87. Genome Surgery
  88. 88. Future of Genomic Medicine? Test when neededWithout information Know your type Blood type Geno type Here is my sequence
  89. 89. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  90. 90. 98 2015 Sci Transl Med. The emerging field of mobile health
  91. 91. 100
  92. 92. NFC 혈당측정기 101
  93. 93. 데이터베이스 기반 혈당관리 102
  94. 94. 103
  95. 95. 혈당 변화 실시간 모니터링 저녁 식사전 고혈당 104
  96. 96. 구글 헬스 앱 분야 매출 1위 ‘눔(noom)’ 105
  97. 97. 106
  98. 98. 107
  99. 99. 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 108
  100. 100. 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 109
  101. 101. 운동량 실시간 모니터링 0 5000 10000 15000 20000 25000 걸음 수 운동X 110
  102. 102. 111
  103. 103. 운동 식사 112
  104. 104. 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 113
  105. 105. 114
  106. 106. 115
  107. 107. 피부 사진 원격 진단/처방 116
  108. 108. 117
  109. 109. 심전도 원격 진단/처방 118
  110. 110. 119
  111. 111. 120http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=report_s
  112. 112. 121 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits November 3, 2015
  113. 113. 122 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits
  114. 114. Personalized Nutrition by Prediction of Glycemic Responses 123 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses Received: October 5, 2015; Received in revised form: October 29, 2015; Accepted: October 30, 2015;
  115. 115. 124 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  116. 116. 125 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  117. 117. 126 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
  118. 118. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  119. 119. Electronic Health Records 2012 NRG Mining electronic health records- towards better research applications and clinical care 128
  120. 120. PCA Analysis혈당 신장기능 129
  121. 121. Machine Learning 2014 Big data bioinformatics130
  122. 122. Clinical Notes 131
  123. 123. 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)132
  124. 124. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
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  129. 129. 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 Cardiology138
  130. 130. 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 Cardiology139
  131. 131. 140
  132. 132. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  133. 133. 2013 Science Structural and Functional Brain Networks- From Connections to Cognition
  134. 134. Green Lines: orexigenic pathways Red lines: dopaminergic pathways 2013 The contribution of brain reward circuits to the obesity epidemic 2014 Obesity – A neuropsychological disease- Systematic review and neuropsychological mode A, amygdala; H, hypothalamus; NA, nucleus accumbens, PFC, prefrontal cortex; VTA, ventral tegmental area 143 tDCS Neuromodulation Controls Feeding Behavior via Food Reward Activity and Connectivity Neuromodulation Brain Activity Feeding Behavior Brain Connectivity
  135. 135. fMRI analysis 2013 Science Functional interactions as big data in the human brain
  136. 136. 2013 Science Functional interactions as big data in the human brain
  137. 137. 147 2013 Science Functional interactions as big data in the human brain 2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns
  138. 138. 2014 Radiomics 2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
  139. 139. 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
  140. 140. Contents 1. Introduction 2. Human Genome Project and Beyond 3. Genome Data ① Cancer Targeted Therapy ② Disease Risk (Common, Rare) ③ Pharmacogenomics ④ Others (Fetal DNA, Microbiome) 4. Sensor/Mobile Data 5. Electrical Health Records 6. National Healthcare Data 7. Medical Images 8. Biomedical Big Data + Artificial Intelligence
  141. 141. 151
  142. 142. 152
  143. 143. Multidimensional Architecture 153 Gene Hormone Psychology Behavior Phenotype Outcome Metabolic Disease Vascular Disease Environment
  144. 144. Healthcare Big Data + Artificial Intelligence 154 Healthcare Big Data Machine Learning Novel Insights and Applications
  145. 145. 155
  146. 146. 157 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
  147. 147. 158 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.
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  149. 149. 160 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
  150. 150. Environment Survey NeuroimagingGenetics Lab/Hormone Hospital Cognitive Personalize Psychotherapy Dietary Intervention Exercise Intervention Food Exercise Glucose Mobile Drug Neuromodulation Monitoring

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