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The Present and Future of Personal Health Record and Artificial Intelligence Healthcare

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1. Why Personal Health Record and Artificial Intelligence ?
2. Obesity Example
3. Personal Health Record
1) Genetic Data
2) Electrical Health Records
3) National Healthcare Data
4) Medical Images
5) Sensor/Mobile Data
6) Data Integration
4. PHR+AI Applications

Published in: Healthcare
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The Present and Future of Personal Health Record and Artificial Intelligence Healthcare

  1. 1. 1
  2. 2. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 2
  3. 3. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 3
  4. 4. Why PHR and AI? 4 Healthcare Big Data Machine Learning Novel Insights and Applications
  5. 5. $215MPrecision Medicine Initiative 2015/1/30
  6. 6. Tipping Point for Big Data Healthcare 2013 McKinsey The big data revolution in healthcare
  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. GWAS  PheWAS Phenotype-wide Association Study
  13. 13. 1000 개의 질병들 Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Phenotype-wide Association Study
  14. 14. Genome-Envirome-Phenome-wide Association Study Phenome-wide (Lab,Diagnosis) Proposal (Choi) Genome-wide Environment-wide (Life style, diet, exercise, pollution)
  15. 15. Anatome-Phenome-wide Association Study 2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants Phenome Anatome
  16. 16. Machine Learning 2014 Big data bioinformatics
  17. 17. 의료 빅데이터의 새로운 역할 전통적인 관점 연구 Large scale (unstructured) data Summary (Modify) Classical hypothesis driven study 새로운 관점 연구 Hypothesis Generating Study
  18. 18. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 21
  19. 19. 22 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
  20. 20. Environment Survey NeuroimagingGenetics Lab/Hormone Hospital Cognitive Personalize Psychotherapy Dietary Intervention Exercise Intervention Food Exercise Glucose Mobile Drug Neuromodulation Monitoring
  21. 21. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 24
  22. 22. 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
  23. 23. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing
  24. 24. 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 27
  25. 25. 저의 유전자 분석 결과를 반영하여 진료 해주세요!! 헠? 28
  26. 26. 30만원-200만원 30
  27. 27. 31
  28. 28. (출처: 금창원 대표님 블로그) $99 TV CF 32
  29. 29. 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 ?
  30. 30. Future of Genomic Medicine? Test when neededWithout information Know your type Blood type Geno type Here is my sequence
  31. 31. 35
  32. 32. 36
  33. 33. 37
  34. 34. 38
  35. 35. 39
  36. 36. Genetics of eating behavior 2011 Genetics of eating behavior
  37. 37. Gene-Environment Interaction Gene Environment Disease Genetic Predisposition Score Sugar-Sweetened Beverages
  38. 38. Soda School No-Soda School Obese Family Lean Family
  39. 39. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 43
  40. 40. Electronic Health Records 2012 NRG Mining electronic health records- towards better research applications and clinical care 44
  41. 41. PCA Analysis혈당 신장기능 45
  42. 42. Machine Learning 2014 Big data bioinformatics46
  43. 43. Clinical Notes 47
  44. 44. 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)48
  45. 45. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 49
  46. 46. 50
  47. 47. 51
  48. 48. 52
  49. 49. 53
  50. 50. Big data platform model by Korea Institute of Drug Safety and Risk Management
  51. 51. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 55
  52. 52. Heart SIMENS: CT Cardio-Vascular Engine
  53. 53. 2013 Science Structural and Functional Brain Networks- From Connections to Cognition
  54. 54. 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 58 tDCS Neuromodulation Controls Feeding Behavior via Food Reward Activity and Connectivity Neuromodulation Brain Activity Feeding Behavior Brain Connectivity
  55. 55. fMRI analysis 2013 Science Functional interactions as big data in the human brain
  56. 56. 2013 Science Functional interactions as big data in the human brain
  57. 57. 62 2013 Science Functional interactions as big data in the human brain 2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns
  58. 58. 2014 Radiomics 2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
  59. 59. 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
  60. 60. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 65
  61. 61. 66 2015 Sci Transl Med. The emerging field of mobile health
  62. 62. 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
  63. 63. 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 68
  64. 64. 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 69
  65. 65. 운동량 실시간 모니터링 0 5000 10000 15000 20000 25000 걸음 수 운동X 70
  66. 66. 71
  67. 67. 운동 식사 72
  68. 68. Social Network and Obesity Prevalence 2013 PLOS One. Assessing the Online Social Environment for Surveillance of Obesity Prevalence
  69. 69. 2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza Google Flu Trends
  70. 70. 76
  71. 71. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 77
  72. 72. 2014 JAMA Finding the Missing Link for Big Biomedical Data
  73. 73. 79
  74. 74. Apple Health App
  75. 75. 83 국가기관 의료기관 연구기관 개인 개인식별 정보통합 정보저장 통계 인공지능 건강관리 정보공개 수집 분석 활용 개인 집단 Overview of Healthcare Data
  76. 76. 2015 Nature Immunology. A vision and a prescription for big data–enabled medicine
  77. 77. Contents 1. Why Personal Health Record and Artificial Intelligence ? 2. Obesity Example 3. Personal Health Record ① Genetic Data ② Electrical Health Records ③ National Healthcare Data ④ Medical Images ⑤ Sensor/Mobile Data ⑥ Data Integration 4. PHR+AI Applications 85
  78. 78. 86
  79. 79. 89
  80. 80. 90 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
  81. 81. 91 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.
  82. 82. 92
  83. 83. Environment Survey NeuroimagingGenetics Lab/Hormone Hospital Cognitive Personalize Psychotherapy Dietary Intervention Exercise Intervention Food Exercise Glucose Mobile Drug Neuromodulation Monitoring
  84. 84. The FUTURE MEDICINE is already at PRESENT 94

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