- 의료 빅데이터 - 개념 입문과 임상의사가 할 수 있는 일 빅데이터 개념 입문과 의료와 관계된 빅데이터 종류와 활용 방법 진료와 연구에 활용할 수 있는 방법을 임상의사의 관점에서 다루었습니다. 1. What is Big Data? 2. Healthcare Big Data 1) Electrical Health Records (EHR) Structured/Unstructured Data 2) Medical Images 3) National Healthcare Data 4) Behavior/Sensor Data 5) Genetic Data 3. Clinical and Research Applications

1. Big Data Driven Healthcare
충북대학교병원 내분비내과 최형진

2. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

3. What is Big Data?

5. 미국 내분비 학회

6. 미국 골대사 학회

7. Big Data Dimensions

8. Big Meal?
Volume Variety

9. Velocity

10. (중환자실 심전도)

11. Data Variety

12. Architecture of Big Data Analytics
2014 Big data analytics in healthcare- promise and potential

13. Machine Learning
2014 Big data bioinformatics

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

15. Gartner's Hype Cycles

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

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

20. GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant

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

22. 2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)

23. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

24. Genomics
Wearable/
Smart Device
Medical Informatics
Genome Sequencing
Quantified Self
Health Avatar
Electronic Medical Records (EMR)
Medical Images (MRI, CT)
National Healthcare data (Insurance)

26. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

27. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care

28. 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)

29. Medication Data

30. Lab Data

33. 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
충북대 정보통계학과
허태영 교수

34. PCA Analysis혈당
신장기능

35. Clinical Notes

36. 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)

39. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

40. Medical
Images

41. Brain Vasculature
Color =
Vessel Diameters
2011 Optimization of MicroCT Imaging and Blood Vessel Diameter Quantitation of Preclinical Specimen Vasculature with Radiopaque Polymer Injection Medium

43. fMRI analysis
2013 Sciencce Functional interactions as big data in the human brain

44. Functional Connectivity
2013 Sciencce Functional interactions as big data in the human brain
N=50,000 voxels
N(N – 1)/2 = 1,249,975,000 Pairs
Seed Region Based Analysis

45. 비만 fMRI 연구
tDCS stimulation
2mA for 20min
Behavioral task 1
(food image ratings)
~7min
3
+
Food presentation
max 4sec
Feedback
1sec
Fixation
1~10sec
Blood test/survey
fMRI
Time
Blood test/survey
Resting MRI
5-6min
T1 structure MRI
5-6min
Behavior task 2
(food intake rating)

46. Bone Quality and
TBS (Trabecular Bone Score)

47. Clinical Implication of TBS
2014 Endocrine. Utility of the trabecular bone score (TBS) in secondary osteoporosis

48. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

50. Overview of secondary data in
public health by data source

51. Data Variety

52. 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 sumission

53. Design
BB
CCBACEI
2007 2011
Hypertension
Fracture Incidence
Fracture Incidence
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
Fracture Incidence
2008

54. Fracture
Fracture Fracture
* Exclude
- Fractures
- Medications
(New User)
Fracture
No
Fracture
BB
BB
BB
BB
BB
ARB
ARB
ARB
ARB
ARB
BB
Group
ARB
Group
First Prescription of Antihypertensives
Fracture Outcomes
2007 2011.1.2008 2009 2010 2011.12.31.
Exclusion of Fracture
within 6 months

56. 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 (%)

57. 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

58. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

66. 혈당관리

67. 혈당 관리

68. 혈당관리

69. Apple Healthbook

70. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ National Healthcare Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

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

72. 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

73. 저의 유전자
분석 결과를
반영하여 진료
해주세요!! 헠?

75. Voxel-wise GWAS
2014 Nature Neuroscience. Whole-genome analyses of whole-brain data- working within an expanded search space

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

77. Disease GWAS vs. Whole-brain GWAS
2014 Nature Neuroscience. Whole-genome analyses of whole-brain data- working within an expanded search space

81. 2014 JAMA Finding the Missing Link for Big Biomedical Data

82. Contents
1. What is Big Data?
2. Healthcare Big Data
① Electrical Health Records (EHR)
Structured/Unstructured Data
② Medical Images
③ Government Data
④ Behavior/Sensor Data
⑤ Genetic Data
3. Clinical and Research Applications

83. 발전한다?

84. New Value Pathways

85. Individual Decision Making

86. Medical Big Data
 Artificial Intelligence

87. Jeopardy!
2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지

91. 2013 PLOS CB Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza
Google Flu Trends

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

93. 2014 Science. Big data. The parable of Google Flu- traps in big data analysis

94. 2014 Science. Big data. The parable of Google Flu- traps in big data analysis

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

97. Medical Big Data

98. Medical Big Data

99. Medical Big Data

101. R vs. Stata vs. SAS

103. 빅데이터 연구 적용
전통적인 관점 연구
Large scale
(unstructured)
data
Summary
(Modify)
Classical hypothesis driven study
새로운 관점 연구
Hypothesis Generating Study