health risk prediction via mining big health data -...

Health Risk Prediction via Mining Big Health Data

Vincent S. [email protected]

Department of Computer ScienceNational Chiao Tung University

Taiwan

Taiwan-Italy bilateral workshop on Smart City, 2015

22

Emerging Needs in Smart City Development

3

Outline

Brief Bio Sketch Introduction Traditional Health Risk Assessment Health Risk Mining and Prediction

Some Recent Developments Large-Scale Population-based Health Data Mining Disease Risk Patterns Mining Health Risk Mining for Chronicle DiseasesEarly

Prediction & Monitoring for Disease Outbreak Concluding Remarks

44

Brief Bio Sketch Dr. Vincent S. Tseng Professional Positions

Professor, Dept. Computer Science, National Chiao Tung University, Taiwan Chair, IEEE Computational Intelligence Society (CIS) Tainan Section Review Board for government units Taiwan, including Ministry of Science

and Technology, Ministry of Health and Welfare Editor board: IEEE Transactions on Knowledge and Data Engineering

(TKDE), IEEE Journal on Biomedicine and Health Informatics (JBHI), ACM Transactions on Knowledge Discovery from Data (TKDD), etc.

President, Taiwanese Association for Artificial Intelligence (2011-2012) Director, Institute of Medical Informatics, NCKU, Taiwan (2008/8-2011/7) Director, Medical Informatics Center, NCKU Hospital (2004-2007)

Has published more than 300 papers in referred journals/conferences related to data mining and intelligent computing; Held/filed more than 15 patents in USA and ROC

Has conducted 50+ academic/industrial research projects

55

Development of Intelligent and High-Performance Big Data Mining Techniques

Applications in Emerging Domains

Biomedical/Health Data Mining

Mobile and Social Network Data Mining

Multimedia Data Mining

Manufacturing Data Mining

Etc.

Main research topics in my research group

6

Cloud, GPU & Stream Computing Platform

Big Data Mining Platform

C ++C ++

ClustersAssociation.

PredictiveModels

Reports

Models/Rules

ODBC

Direct

Custom

Data A

ccess C A

PI

Data A

ccess C A

PI Data

PreparationComponents

Data PreparationComponents

Mining EngineComponents

AssociationRulesAssociationRules

SequentialPatternsSequentialPatterns

ClusteringClustering

ClassificationClassification

Rules RetrieveComponentsRules RetrieveComponents

PredictionComponentsPredictionComponents

ApplicationsModuleApplicationsModule

DataPreparation Deploy

DataAccess

DataModeling Presentation

InterestingPatterns

InputData

7

Applications in Various Domains

8

A large-scale research initiative in 2012 aimed at Innovations around smartphone-based research Collect smartphone data in everyday life conditions Community-based evaluation of related mobile data analysis

methodologies Data source: Lausanne Data Collection Campaign

9

Personal information Media files Calendar Applications

Social information Call log Contacts Bluetooth

User Profile/Behavior Modeling and Prediction Device information

Process Accelerometer System Information

Location information GSM WLAN Sequence of place visits

11

11

Biomedical/Health Data Mining

Gene Expression MiningAssociation pattern analysisClusteringTime-series Analysis

Protein Expression Mining

Mass SpectrometryLC/MS mining

Biomarker & Health Risk Mining Vital sign analysis (ECG/EEG)Disease biomarker analysisHealth Risk AssessmentTele-care platform Patient Behavior Mining

Gene Regulation Network AnalysisProtein Structure Mining

Data Mining TechniquesAssociation Rule MiningSequential Pattern MiningClusteringClassificationTime-series Analysisetc.

12

12

Trends of Medicine & Healthcare

Medicine Personalized Medicine Personalized treatment

Preventive Medicine Early detection

Preventive Healthcare Personalized Preventive Risk Assessment

13

Health Risk Assessment (HRA)- General health examination -

Examinee

Assessed &Interpretation

Health examination report

suggestions

Doctors

Is there some potentialhealth risk?

In general health examination, health conditions are diagnosed and assessed simply as normal/abnormal by Lab results

Lack of predictive assessment on health riskHealth

Examination

0.88Waist-Hip Ratio (male

14

Historic Health Examination data

HRA System

Traditional Health Examination System

Traditional Health Examination ReportMr. Chen

Examined Date: 2009/05/03Weight:59 kgHeight:165.0 cmBlood Pressure: 120~70

Examined Date:2008/04/04Weight:65 kgHeight:165.2 cmBlood Pressure: 110~70

Examined Date:2007/10/16Weight:64 kgHeight:165.1 cmBllod Pressure: 120~70

Examined Date:2005/09/30Weight:61 kgHeight:165.2 cmBlood Pressure: 110~70

14

Predictive HRA- Scenario-

15

HRA System

Traditional Health Report

Integrated Report

Doctor

Mr. Chen

Diabetes Prediction Model

Pneumonia Prediction Model

Heart Disease Prediction Model

Apoplexy Prediction Model

Malignancy Prediction Model

Predictive HRA (cont.)- Scenario-

16

Feature Set

Disease Prediction Related Work

[Huang et al., 2007]

Clinical examination

Clinical examination Feature weighting

Naive Bayes

C4.5

IB1

ClassificationFeature selection

Physician selection

Good

Bad

17Intelligent Database Laboratory, CSIE, NCKU - 17 -

Disease Prediction Related Work (cont.)

18


[Palaniappan et al., 2008] Intelligent Heart Disease Prediction System (IHDPS)

- 18 -

19

[Patil et al., 2009]


Heart DiseaseDatabases

Weighted

Clustered using K-means

Frequent Pattern Mining

Preprocessing

Heart Attack Prediction System

Process

20

Predictive Risk Study of Hepatocellular Carcinoma (HCC)- [Chen et al. JAMA06]

Prospective cohort study of 3653 participants (aged 30-65) in Taiwan

Six main indicators Sex Age cigarette smoking alcohol consumption serostatus for the hepatitis B e antigen (HBeAg) serum alanine aminotransferase level

Elevated serum HBV DNA level (> or =10,000 copies/mL) is a strong risk predictor of hepatocellular carcinoma, independent of HBeAg, serum alanine aminotransferase level, and liver cirrhosis.

21

Taiwan offers new models to predict Hepatitis C cancer risk

Announced in 2012 Conference of the Asian Pacific Association for Study of the Liver by Dr. CJ Chens team

The model delivers prediction results with 80% accuracy based on a large-scale population screening study in Taiwan

Main indicators Age Liver function indexes ALT and AST Hepatitis C virus RNA in serum Genotype of the virus Liver cirrhosis

Free Smartphone App provided for use

22

Neural NetworkStatistics

Health Risk Assessment - Disease Prediction -

Traditional prediction mechanisms were built based on static and simple view on health/medical records.

Physical Body height (cm) 166

Body weight (kg) 62.8

BMI (18.5~24) 22.8

Systolic blood pressure (110~140) 120

Diastolic blood pressure (60~90) 85

Waist circumference (cm) 79

Hip circumference (cm) 89

Waist-Hip Ratio (male

23

Health Risk Prediction- Divination for Health?

What have we seen? A snapshot? (point) Temporal evolution?

(segment) Full view? (full coverage)

24

Challenges

Big data with heterogeneous data sources Volume, Variety, Velocity, etc.

Data quality problem Data imbalance problem Post-processing of large analyzed/extracted results Need of deep incorporation of medical domain

knowledge Privacy issue

Some Recent Developmentsin Taiwan

25

Large-Scale Population-based Health Data Mining

26

Health-related information are derived from various data

One hospital data Through electronic medical record (EMR) system we could calculate the

incidence of event B on patients receiving treatment A. Limitation: patients with event B might be diagnosed in another hospital

Question: whats the probability of occurrence of adverse event B after taking treatment A?

Several hospitals data Through EMR exchange system we could have a more accurate estimate

of number of incidence of event B. Limitation: difficult to detect rare event B

All hospitals population-based data Through National Health Insurance Claims Data we could have very

large patient sample size to detect rare event B.

27

Micro view through hospital-based data

Conclusions from hospital A

Conclusions from hospital B

28

Macro view through bigger data

29

Occurrence of febrile convulsion

Frightened mother would ask doctor: what is the probability that my child will become a patient with epilepsy?

Febrile convulsion as an example

30

The probability varied greatly with different datasets

Mainly due to referral

bias

31

Importance of using big health data to detect drug adverse effects

Lancet 2005;365:475-481

32

33

National Health Insurance Research Databasein Taiwan

National Health Insurance (NHI ) Established in March 1, 1995 Serves 99.2% of Taiwanese population (20M+) Covers 92.62% of medical institutions

Longitudinal Health Insurance Database ( LHID ) sampled from NHIRD Including 951,044 people health records 1997 now

Strongly representative in Taiwan Every living regions Big time interval

15+ years

Reference : National Health Insurance, http://www.nhi.gov.tw, 2012

Evolution of value-added analysis of health datasets in Taiwan

CR

2000 2005 2010

1G NHI

2G NHI

2015

CODBR

3G Lab data & Patient centered outcomeCloud

computing

CRNHI CODBR

NHI: National Health InsuranceBR: Birth RegistryCR: Cancer RegistryCOD: Cause of Death Mortality Lab: Laboratory data

34

Incorporation of more heterogeneous datasets

Lab data & Patient reported outcome

Cloud computing

CRNHI CODBR

Sensor-based biomarker monitoring data

Smart Health RiskAlert

Environmental monitoring data

35

36

Rich Topics for Explorations

Taiwans government units have launched large-scale projects for value-added analysis on the national health data: Disease markers discovery Disease progression model Adverse drug reactions (ADRs) Medication redundancy Public health issues Privacy issue .. etc.

37

DB1

Databases

DB2 DBn

Data Loading

DB Service

DB Server

Data IntegrationData ProcessingQuery Interface

VisualizationData Output

Data Download

USER

DB

Data Analytics Service

Cloud-based Data Mining Components

Mining Result

Cloud System

Integrated Database &Data Analytics Services

DataMigration

Integrated Platform for Big Health Data Analytics



38

Goal: Healthcare as a Service

Data Cloud Computing Cloud

Disease Risk Patterns Mining

39

40

Goal

To develop an effective framework for

1. Mining disease risk patterns

For further medical research

2. Assessing disease risk

Identify potential patients for health monitoring and diagnosis assistance

41

System Framework

42

System Main Frame

43

Pattern Annotation & Visualization Frame

44

Case Study: Chronic Kidney Disease (CKD)

Leads to End-Stage Renal Disease (ESRD) Dialysis High cost of NT$30 billion per year

Not easy to be found at early stage

Not a single specific disease

Multiple and complex causes

Reference : UNITED STATES RENAL DATA SYSTEM, 2013 Atlas of End-Stage Renal Disease

45

Well-Known Related

Risk Pattern ICD-9-CM Definition of Ancestors Support Confidence PubMed Search

{40190}CKD 40190: Essential hypertension, unspecified 18.05% 76.41% 83

{25000}CKD

25000: Diabetes mellitus without mention of complication, type II, or unspecified type, not stated as uncontrolled

14.57% 80.78% 1313

Potentially Surprising

Risk Pattern ICD-9-CM Definition of Ancestors Support Confidence

{53300}CKD 53300:Peptic ulcer, site unspecified

14.42% 65.56%

{30000}CKD 30000:Anxiety state

9.22% 64.83%

{52300}CKD 52300:Gingival and periodontal diseases

37.71% 57.63%

Example Risk Patterns Discovered

46

Health Risk Mining for Chronicle Diseases

47

Health WarningDiscover potential riskusing trend analysis

The current health examination report did not carry out trend analysis

Mining of Potential Health Risk Trend

200 mg/dl

Value

Date2006/03 2007/02 2008/03 2009/02triglyceride values

200 mg/dl

Value

Date2006/03 2007/02 2008/03 2009/02cholesterol values

48

Health Risk Mining and Prediction- in Large-Scale and Dynamic View

Doctors

Risk Pattern Mining&

Health Risk Prediction

Examinee

0.88Waist-Hip Ratio (male

4949

General System Framework

Integrateddataset

Health Risk Pattern Mining

Doctor

Disease Predictor Building

Parameter Setting

Profiledataset

Valuedataset

Reportdataset

DiseasePrediction

Model

Feature Integration

Health Risk Patterns

Phase 1. Health Risk Pattern Mining

Phase 2. Model Construction

Health check

PredictionResults

Phase 3. Risk Prediction

Integrateddataset

50

Key Steps

Feature Selection Pattern Mining Frequent pattern mining, surprising pattern mining, etc.

Modeling Decision tree, SVM, Neural network, etc

Prediction Ensemble, etc

51

Health Examination Data of a Medical Center in Taiwan Time Period: February, 1996 ~ August, 2009 Total Number of subjects:14,218 Target Disease: Diabetes Disease on Fasting Plasma Glucose (FPG)

51

Item Item Item Item ItemTriacylGlycerol Waistline Systolic pressure

(Left Hand)Diastolic Pressure(Right Hand)

HbA1c

HDL-C Arm girth Sphygmus(Right Hand)

Diastolic Pressure(Left Hand)

Fasting Plasma Glucose

Total-cholesterol

Weight Sphygmus(Left Hand)

Diastolic Pressure(Before Stand)

Height Systolic pressure (RightHand)

Sphygmus(Before Stand)

OGTT

Experimental Evaluation

52

Experimental Results (FPG)

0%10%20%30%40%50%60%70%80%90%

100%Accuracy Precision Sensitivity Specificity F-Measure

AllFemaleMale

53

Experimental Results (High Dense Lipoprotein)

0%10%20%30%40%50%60%70%80%90%

100%Accuracy Precision Sensitivity Specificity F-Measure

AllFemaleMale

54

>0 Represent the existence of the health risk patte100) 5.0The 5 records are classified into C3 by the rule

(Note that they are correctly classified.)

Health Risk Pattern - HbA1c (F2) (A238):


Health Risk Pattern - Diastolic Pressure(Left Hand) (F17) (A87):


Decision Tree

55- 55 -



Health Risk Pattern - Diastolic Pressure (Left Hand) (F17) (A87):


Target Attribute: Fasting plasma glucose (FPG)C1Represent unhealthy range (100)

Decision Tree (cont.)

56

Health Examination Historic Data

Our System

Assume five common chronic disease prediction models have been built via our health analysis system from historic health examination data.

Outpatient Data

Diabetes Prediction Model

Pneumonia Prediction Model

Heart Disease Prediction Model

Apoplexy Prediction Model

Malignancy Prediction Model

Practical Application - Scenario-

Early Prediction & Monitoring for Disease Outbreak

- Case Study on Asthma Care

57

5858

Asthma Care

Asthma is a chronic disease

Airway constrict Apply MDI When asthma attacks!

- Potential triggers

Cold airWarm, moist airAllergens Stress Cold

59

Prediction of Asthma Outbreak

Sliding window Size: 5

day1 day2 day3 day4 day5

day6

Combined Data

Server

Asthma OutbreakPrediction

60

60

Integrated Data Mining Mechanism

Data Mining Understandable Pattern DB

Association Rule

Sequential Pattern

Time Series

Classification

Predictive Alarm Engine

Symptom

Factor

Factor

SymptomFactor

EnvironmentDataset

Chronic Disease Patients

Bio-SignalDataset

UserProfiles

6161

Data Mining Workflow

UserProfiles

EnvironmentDataset

AsthmaSymptomDataset

Data Pre-Processing

Sequential Pattern MiningPattern Mining

Classification MiningRule Mining: PBD

Classification MiningRule Mining: PBC

{1st asthma symptom} Potential Asthma{5th allergy symptom} Potential Asthma

{PM10 is low, Moderate temperature} None Asthma

Predictive Alarm Engine

Phase 1Phase 2

7 8

0 1

4

3

0

5

2

1

7 8

0 1

4

3

0

5

2

1

Testing DatasetTraining Dataset

Integrated Dataset

62

Integrated Data: Patient SymptomsAsthma

SymptomDataset

Asthma Sympto

ms

Fever Sympt

omNighttime Symptom Daytime Symptom

0 NO Sleep well No cough, exercise regularly

1 Yes Sleep well, but with intermittent cough Intermittent dry cough

2 Wake up coughing and can fall asleep after inhaling steroids Cough with phlegm, cough when exercising

3 Serious cough and fall asleep hardly Wheezing, use of inhaled vasodilators

4 Short breath, incessant cough, need medicine and go to hospital immediatelyAllergy Symptom

Nose Symptom eye symptom skin symptom

0 Itchy and rubbing normal normal

1 sneeze rubbing Itchy, no swelling

2 Nasal congestionSwelling and Photophobia

Local swelling

3 Running noseMore than 2 rash

blocks

63

Weather Information Source data: Central Weather Burau, Taiwan

Temperature Humidity Highest Temperature Lowest Temperature Temperature

Difference

IntegratedData:EnvironmentData

64

Air Pollutants Source Data: Open Environmental Database, Taiwan

Air pollution attributes:

IntegratedData:EnvironmentData(cont.)

65

Example of Decision Tree Output

Yes

2nd allergy symptom

PM10 is high Low humidity

Catch a cold

5th asthma symptom

3rd allergy symptom

PotentialAsthma

High temperature difference

PotentialAsthma

None PotentialAsthma

YesNo

YesNo

YesNo

Yes


No

YesNo

PotentialAsthma

Yes

PotentialAsthma


No

66

Example Induction Rules

,inhaledmaintenancemedicineandbronchodilator Highrisk[sup:14.16%,conf:100%]

,noneedmedicine Normal[sup:5.584%,conf:100%]

,noneedmedicine,differenceintemperature:2,theindicatorofpollutants:O3 Normal[sup:5.076%,conf:100%]

,noneedmedicine,,noneedmedicine,humidity:pattern(up,flat)Highrisk[sup:0.761%,conf:100%]

,inhaledmaintenancemedicineor oralmaintenancemedicinedayandnight,,inhaledmaintenancemedicineand oralmaintenancemedicinedayandnight,Thequalityofair:good general Highrisk[sup:2.792%,conf:100%]

,inhaledmaintenancemedicineororalmaintenancemedicinedayandnight,,inhaledmaintenancemedicineand oralmaintenancemedicinedayandnight,differenceintemperature:8 Highrisk[sup:3.553%,conf:100%]

,inhaledmaintenancemedicineororalmaintenancemedicinedayandnight,thequalityofair:general good bad,PM10:50150 Highrisk[sup:7.614%,conf:100%]

67

PerformanceofClassifiers

0.00%

10.00%20.00%

30.00%

40.00%

50.00%

60.00%70.00%

80.00%90.00%

Air pollution Weather Asthma Combined

Datasets

Average of 10 Experiments: PBC Summary

Precision (Out)

Recall(Out)

68

GPRS

Intelligent Mobile Healthcare: Framework

Patient Medical Station/ Clinician

InternetAccess online

Query Online

Query by cell phone Urgent event notice

Show messages

AutomaticTransmit

Medical Center

Database

Server

GPRS

Data mining systemPatient monitoring system

EnvironmentalInformation

69

Location-Aware Asthma Alert

Data Mining System

Predictive Alert System

70

Concluding Remarks

Points -> Segment -> Coverage

Full view for health risk prediction!

71

Concluding Remarks (cont.)- A Highly Integrated Framework for Smart Healthcare via Big Data Mining

Thanks for your attention&

Look forward to collaborations

health risk prediction via mining big health data -...

Documents