유전체의학과 미래의학 2 스마트 의학_빅데이터_공개용

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유전체의학과 미래의학 2 스마트 의학_빅데이터_공개용

  1. 1. Genomics Big Data Wearable/ Smart Device Medical Informatics Genome Sequencing Data Science Quantified Self Health Avatar
  2. 2. Wearable and Medicine
  3. 3. Emergency!!
  4. 4. Heart Monitor of AliveCor
  5. 5. SAMSUNG Gear
  6. 6. Evolution of Diabetes Control Insulin Glucose Insulin Injection Episodic Glucose Monitoring Continuous Insulin Injection Continuous Glucose Monitoring Artificial Pancreas
  7. 7. Google’s Smart Contact Lenses Glucose Monitoring
  8. 8. Smart/Mobile Healthcare
  9. 9. KDNP 당뇨병수첩 – NFC 기반 혈당 결과 무선 전송 기능 포함
  10. 10. Date Calorie Budget Calories Logged Breakfast Cals Lunch Cals Dinner Cals. % Red % Yellow % Green 2014-05-23 1,400 1,325 386 463 476 29.00% 70.00% 0.00% 2014-05-22 1,400 1,672 238 566 868 28.00% 71.00% 0.00% 2014-05-19 1,400 196 196 0 0 0.00% 100.00% 0.00% 2014-05-17 1,400 695 283 412 0 0.00% 100.00% 0.00% 2014-04-05 1,400 477 477 0 0 0.00% 0.00% 100.00% 2014-03-22 1,400 576 208 368 0 36.00% 0.00% 63.00% 2013-12-15 1,400 800 300 200 300 37.00% 62.00% 0.00% 2013-08-14 1,400 665 0 0 665 0.00% 54.00% 45.00% 2013-08-10 1,400 150 0 150 0 0.00% 100.00% 0.00% 2013-08-09 1,400 440 440 0 0 54.00% 0.00% 45.00% 2013-07-05 1,400 200 200 0 0 0.00% 100.00% 0.00%
  11. 11. 애니팡 성공 – SNS 경쟁
  12. 12. Apple Healthbook
  13. 13. Genomics/ Biomarker Big Data/ Medical Informatics Smart Device/ Wearable Genome Sequencing Data Science Quantified Self Health Avatar Mobile Health Machine Learning
  14. 14. Diabetic patients (n = 12,074; 35,545 fundus examinations) The time from onset of retinopathy to clinical diagnosis of diabetes was calculated as a point estimate by extrapolating the intercept of the best-fitting regression line with the horizontal axis. 2014 DC Estimating the Delay Between Onset and Diagnosis of Type 2 Diabetes From the Time Course of Retinopathy Prevalence
  15. 15. 2014 DC Estimating the Delay Between Onset and Diagnosis of Type 2 Diabetes From the Time Course of Retinopathy Prevalence
  16. 16. 의료 정보학 Medical Informatics • 처방전달 시스템(OCS) • 영상정보 저장전달 시스템(PACS) • 전자의무기록(EMR) • 의료 빅데이터 • 인공지능
  17. 17. 빅 데이터 분석 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
  18. 18. AFTER • Dialysis pts, Hb <11 g/dL • Non-dialytic CKD eGFR <30 mL/min/1.73m2 Hb <10 g/dL BEFORE • Dialysis pts, Hb <11 g/dL - 6m 0m 6m 12m Reimbursement change (pint/person-year) Transfusion requirement
  19. 19. …………………….
  20. 20. Medical Big Data
  21. 21. Big data platform model by Korea Institute of Drug Safety and Risk Management
  22. 22. Future drug safety monitoring system based on the big data
  23. 23. Overview of secondary data in public health by data source
  24. 24. Medical Big Data
  25. 25. Medical Big Data
  26. 26. 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., in revision
  27. 27. Fracture rates per 10,000 person-years (95% CI) 819 Fracture Rates (per 10,000 Person-Years) Total Male Female AB: alpha-adrenergic blocker ACEI: angiotensin converting enzyme inhibitor DIUR: diuretics CCB: calcium channel blocker BB: beta-adrenergic blocker ARB: angiotesin-receptor blocker
  28. 28. 2008-2011 Prescriptions N = 2,886,555 New Users N = 718,293 Monotherapy+Combination (MPR>80%) Age > 50 yrs No previous fracture N = 208,648 심평원 빅데이터 연구 당뇨병약과 골절
  29. 29. Total 0 200 400 600 Metformin+DPP4i SU+TZD Metformin+TZD Metformin+SU AGI SU Metformin Non-user Fracture Rates per 10,000 Person-Years Female 0 200 400 600 Metformin+DPP4i SU+TZD Metformin+TZD Metformin+SU AGI SU Metformin Non-user Fracture Rates per 10,000 Person-Years Fracture Rates (per 10,000 Person-Years) 0 200 400 600 Metformin+DPP4i SU+TZD Fracture Rates per 10,000 Person-Years Male 0 200 400 600 Metformin+DPP4i SU+TZD Metformin+TZD Metformin+SU AGI SU Metformin Non-user Fracture Rates per 10,000 Person-Years Total Male Female
  30. 30. Medical Big Data  Artificial Intelligence
  31. 31. Jeopardy! 2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지
  32. 32. Medical Big Data  Artificial Intelligence
  33. 33. 2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza Google Flu Trends
  34. 34. Social Network and Obesity Prevalence 2013 PLOS One. Assessing the Online Social Environment for Surveillance of Obesity Prevalence
  35. 35. 2014 JAMA Finding the Missing Link for Big Biomedical Data
  36. 36. Genomics Big Data Wearable/ Smart Device Medical Informatics Genome Sequencing Data Science Quantified Self Health Avatar
  37. 37. http://www.yoonsupchoi.com
  38. 38. Any Questions? • 최형진 • Hyung Jin Choi • hjchoimd@gmail.com • www.facebook.com/hyungjin.choi.75

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