임상의사 관점의 의료빅데이터 연구와 임상적용 - From Clinic To Data, From Data To Clinic

임상의사 관점의 의료빅데이터 연구와 임상적용

From Clinic to Data, From Data to Clinic

Contents

1. What is Healthcare 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

미국 내분비 학회

미국 골대사 학회

What is Big Data?

Big Data Dimensions

Big Meal?

Volume Variety

Velocity

Data Variety

Architecture of Big Data Analytics

2014 Big data analytics in healthcare- promise and potential

Machine Learning

2014 Big data bioinformatics

Tipping Point for Big Data Healthcare

2013 McKinsey The big data revolution in healthcare

Gartner's Hype Cycles

Data Science Big Data

Hypothesis Driven Science Data Driven Science

Hypothesis

Collect

Data

Data

Generate

Hypothesis

Analyze

Analyze

Candidate Gene

Approach

Genome-wide

Approach

Choose a Gene

from Prior Knowledge

Analyze the Gene

Analyze ALL Genes

Discover Novel Findings

GWAS

(Genome wide association study)

SNP chip

Whole Genome

SNP Profiling (500K~1M SNPs)

Common Variant

Estrada et al., Nature Genetics, 2012

+ novel targets

for bone biology

Recent largest GWAS

GEFOS consortium

2010 An Environment-Wide

Association Study (EWAS) on Type

2 Diabetes Mellitus

Environment-Wide Association Study (EWAS)

다양한 환경인자들

GWAS PheWAS

Phenotype-wide Association Study

1000 개의 질병들 Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.

Marylyn D. Ritchie

Contents




② Medical Images



⑤ Genetic Data


Genomics

Wearable/

Smart Device

Medical Informatics

Genome Medicine

Quantified Self

Health Avatar

Electronic Medical Records (EMR)

Medical Images (MRI, CT)

National Healthcare data (Insurance)

Contents




② Medical Images



⑤ Genetic Data


Electronic Health Records

2012 NRG Mining electronic health records- towards better research applications and clinical care

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)

Medication Data

Lab Data

Big Data Analysis

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.81/Creatinine

한 환자의 10년간 신장기능의 변화 전체 환자의 당 조절 정도 분포

충북대 정보통계학과

허태영 교수

PCA Analysis 혈당

신장기능

Clinical Notes

밤동안 저혈당수면 Lt.foot rolling Keep떨림,

식은땀, 현기증, 공복감, 두통, 피로감등의 저혈당 에 저혈당 이 있을 즉알려주도록 밤사이 특이호소 수면유지상처와 통증 상처부위 출혈

oozing, severe pain 알리도록 고혈당 처방된 당뇨식이의 중요성과 간식을 자제하도록 .고혈당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위

oozing Rt.foot rolling keep드레싱 상태를 고혈당 고혈당 의식변화 BST 387 checked.고혈당으로 인한 구강 내 감염 위해 식후 양치, gargle

등 구강 위생 격려.당뇨환자의 발관리 방법에 .

목표 혈당, 목표 당화혈색소에 .식사를 거르거나 지연하지 않도록 .식사요법, 운동요법, 약물요법을 정확히 지키는 것이 중요을 .처방된 당뇨식이의 중요성과 간식을 자제하도록 .고혈당 ,,관리 방법 .혈당 정상 범위임rt foot rolling

중으로 pain호소 밤사이 수면양호걱정신경 예민감정변화 중임감정을 표현하도록 지지하고

경청기분상태 condition 조금 나은 듯 하다고 혈당 조절과 관련하여 신경쓰는 모습 보이며 혈당

self로 측정하는 모습 보임혈당 조절에 안내하고 불편감 지속알리도록고혈당 고혈당 의식변화 고혈당 허약감 지남력 혈당조절 안됨고혈당으로 인한 구강 내 감염 위해 식후 양치, gargle

등 구강 위생 격려.당뇨환자의 정기점검 내용과

빈도에 .BST 140 으로 저혈당 호소 밤동안 저혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현기증, 공복감, 두통, 피로감등의 저혈당 에 저혈당 이 있을 즉알려주도록 pain 및 불편감 호소 WA 잘고혈당 고혈당 의식변화 고혈당 허약감 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, 경구투약,수분 섭취량,대체 탄수화물,의료진의 도움이

필요한 사항에 교혈당 정상 범위임수술부위

oozing Rt.foot rolling keep수술 부위 (출혈, 통증, 부종)수술부위 출혈 상처부위 oozing

Wound 당겨지지 않도록 적절한 체위 취하기

설명감염 발생 위험 요인 수술부위 출혈 밤동안

저혈당 호소 수면 양상 양호rt foot rolling유지하며 감염이나 심한 통증등 침상안정중임BST

420 checkd. R1 알림 obs 하자Rt DM foot site

pain oozing 없는 상태로 저혈당 은 낮동안에는 . BST BST 347 mg /dl checked. R1notify 후

apidra 10U sc 주사.안전간호 시행안전간호 시행

간호기록지 Word Cloud

Natural Language Processing (NLP)

Contents




② Medical Images



⑤ Genetic Data


Medical

Images

Heart

SIMENS: CT Cardio-Vascular Engine

ER Multiple Rule-Out Scan of a

Patient With Disturbance of

Consciousness and Without Breath-

Hold.

SOMATOM Definition Flash

Scanning

Author: Junichiro Nakagawa, MD,*

Isao Ukai, MD*, Osamu Tasaki, MD,

PhD*, Tomoko Fujihara**

and Katharina Otani, PhD ***

Date: Dec 13, 2010

* Trauma and Acute Critical Care

Center, Osaka University Hospital,

Osaka, Japan

**CT Marketing Department,

Healthcare, Siemens Japan K.K.,

Tokyo, Japan

***Research & Collaboration

Development Department,

Healthcare, Siemens Japan K.K.,

Tokyo, Japan

CT Angiography

Brain Vasculature

Color =

Vessel Diameters 2011 Optimization of MicroCT Imaging and Blood Vessel Diameter Quantitation of Preclinical Specimen Vasculature with Radiopaque Polymer Injection Medium

fMRI analysis

2013 Sciencce Functional interactions as big data in the human brain

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

Obesity fMRI Research

Preliminary Pilot Results

p<0.005 uncorrected

Activation of visual, motor and prefrontal brain area in response to visual food cue

3

+

Food presentation

max 4sec

Feedback

1sec

Fixation

1~10sec

Choi et al., on going

Resting state fMRI analysis

Complex network approach

Parcellation

116 brain regions

by AAL map

Adjacency matrix Network properties

node degree, clustering coefficient

Choi et al., on going

Bone Quality and

TBS (Trabecular Bone Score)

Clinical Implication of TBS

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

Contents




② Medical Images



⑤ Genetic Data


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

Number of patients with medical treatments related to

osteoporosis in each calendar year

2005 2006 2007 20080

500

1000

1500Male

Female

Calendar year

Nu

mb

er (

thou

san

d)

2011 JBMM Burden of osteoporosis in adults in Korea- a national health insurance database study

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., in submission

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 2011 2008

Distribution of ARB MPR

(Histogram)

ARB Non-user

20

Fre

qu

en

cy D

en

sity

ARB user

80 120

Medication Possession Ratio (MPR)

Total prescription days

Observation days

350 days (Prescription)

365 days (Observation)

MPR

96%

MPR (%)

Data Variety

NA09-011,10-004_당뇨병_환자에서_심혈관계질환_발생_예방을_위한_아스피린_사용양상_분석

NA09-011,10-004_당뇨병_환자에서_심혈관계질환_발생_예방을_위한_아스피린_사용양상_분석

Impact factor: 8.562

Impact factor: 2.917

Contents




② Medical Images



⑤ Genetic Data


NFC 혈당측정기

데이터베이스 기반 혈당관리

혈당 변화 실시간 모니터링

저녁 식사전 고혈당

구글 헬스 앱 분야 매출 1위 ‘눔(noom)’

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 2014-08-10 2 방울토마토 0 8 4개 (60g) 60 g 2014-08-10 2 방울토마토 0 8 4개 (60g) 60 g 2014-08-10 2 방울토마토 0 8 4개 (60g) 60 g 2014-08-10 2 김밥 1 318 1줄 (200g) 200 g 2014-08-10 3 복숭아 0 91 1개 (269g) 268 g 2014-08-10 4 잡곡밥 0 161 1/2공기 (105g) 105 g 2014-08-10 4 복숭아 0 91 1개 (269g) 268 g 2014-08-10 4 김치 0 3 1/4소접시 (10g) 12 g 2014-08-10 4 장조림 1 66 1/2소접시 (45g) 45 g 2014-08-10 4 콩나물국 0 14 3/4국그릇 (187.5ml) 83 g 2014-08-10 5 포도 0 45 3/4대접시 (75g) 75 g

식사량 실시간 모니터링

0

100

200

300

400

500

600

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

아침

아침

간식

점심

점심

간식

저녁

저녁

간식

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

운동량 실시간 모니터링

0

5000

10000

15000

20000

25000

걸음 수

운동X

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

Contents




② Medical Images



⑤ Genetic Data


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

저의 유전자

분석 결과를

반영하여 진료해주세요!! 헠?

Cancer Targeted Therapy

Targeted Therapy Genetic Test Cancer

Disease Risk and Pharmacogenomics

2012 European Heart Journal. Personalized medicine: hope or hype?

Promise of Human Genome Project

Predicting Complex Diseases

2013 NG Predicting the influence of common variants

“For most diseases, it should be possible to identify the individuals with the highest

genetic risk. However, if the aim is to identify individuals with just twice the mean

population risk, we cannot currently do that with SNPs”

Mean population risk

Highest genetic risk

x2

Dis

ease R

isk

Rare Variant?

Variants and Disease Susceptibility

2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges

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

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.

105

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

2008 HMG Genome-based prediction of common diseases- advances and prospects

2008 HMG Genome-based prediction of common diseases- advances and prospects

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 heritability

2013.01.29

2014.7. 18개 대형병원

FDA Halt 23andMe

2013.11.22.

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.

113

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

Laboratory Director

강현석

Mendelian (single-gene) genetic

disorder

Known single-gene

candidates testing

Whole Genome or Whole

Exome Sequencing

2012 European Heart Journal. Personalized medicine: hope or hype?

Herceptin

Glivec

2013.11.14.

141 drugs

158 labels

60 indications/contraindications Atorvastatin, Azathioprine, Carbamazepine, Carvediolol, Clopidogrel, Codein, Diazepam…..

CPIC 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 dose rs4986893 (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

$99

N>400,000

Future of Genomic Medicine?

Test when needed Without information Know your type

Blood

type

Geno

type

Here is my

sequence

Genome Big Data + Brain Big Data

Voxel-wise GWAS

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

Connectome-wide GWAS


Disease GWAS vs. Whole-brain GWAS


511,089 SNPs

Illumina Beadchip

Functional MRI

N = 1,620

2014 PLOS GENT. Global genetic variations predict brain response to faces

2014 PLOS GENT. Global genetic variations predict brain response to faces

Connectivity of face network

2014 JAMA Finding the Missing Link for Big Biomedical Data

Contents




② Medical Images

③ Government Data


⑤ Genetic Data


발전한다?

New Value Pathways

Individual Decision Making

Clinical and Research Applications

1. Clinical Decision Support

2. Public Health Surveillance

3. Personal Health Record

4. Healthcare Data Integration






Medical Big Data

Artificial Intelligence

Jeopardy!

2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지

Risk Prediction






Social Network and

Obesity Prevalence

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

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

Google Flu Trends

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

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

독감 예보

Big data platform model by Korea Institute of

Drug Safety and Risk Management






Apple Health App






Pros and Cons of Different Data Sources

2014 ASBMR meet the professor handbook

Data Sharing

2014.08.14

insurance claims, blood tests, medical histories

US$10-million pilot study N=30M

http://hineca.tistory.com/entry/Vol11-3%EC%9B%94%ED%98%B8-%EB%B3%B4%EA%B1%B4%EC%9D%98%EB%A3%8C%EC%9D%B4%EC%8A%88-

%EA%B7%BC%EC%8B%9C%EA%B5%90%EC%A0%95%EC%88%A0

Medical Open Innovation Connectivity

(MOIC)






5. How to Prepare for Big Data Driven

Healthcare

R vs. Stata vs. SAS

빅데이터 연구 적용

전통적인 관점 연구

Large scale

(unstructured)

data

Summary

(Modify)

Classical hypothesis driven study

새로운 관점 연구

Hypothesis Generating Study

Health & Medicine

임상의사 관점의 의료빅데이터 연구와 임상적용 - From Clinic To Data, From Data To Clinic