Challenges and Roadmap fMhi L i ffor Machine Learning from Medical Data StreamsMedical ...users.med.up.pt/pprodrigues/lmds11/lemeds2011/McGr… · · 2011-10-24fMhi L i ffor Machine

Challenges and Roadmap f M hi L i ffor Machine Learning from

Medical Data StreamsMedical Data StreamsCarolyn McGregory g

Canada Research Chair in Health Informatics, ProfessorFaculty of Business and IT/Faculty of Health Sciencey y

University of Ontario Institute of [email protected]

• Multi-disciplinary ResearchMulti disciplinary Research• Signal acquisition infrastructures

D t i iti i f t t• Data acquisition infrastructures• Population-based KDDM infrastructures• Real-time processing infrastructures• Real-time Learning InfrastructuresReal time Learning Infrastructures• Inconsistencies of information reported in

researchresearch• Knowledge Translation

Health Health andHealthInformatics

and IT

Medicine Expertsand IT

Information Technology and Computer Systems

Research




Care of the

Preterm infant

Multidisciplinary human resourceshuman resources

GTA CommunityInterpreter ServicesC lt l iti

Neonatologists

Diagnostic ImagingSub-Specialties

Bloorview

GTA Community Hospitals/Hospices

MSH/WCHRI

Interpreter ServicesCultural communities

N t l i t

Baby/Family

NeonatologistsSurgeonsNurses

S i l W k Ph

InfectionControl NPs

oo e MSH/WCHRI

Baby/family

NeonatologistsSurgeonsNurses

NPs Residents/Fellows U of TBaby/Family

RTs

Social Work

DietaryRehabPalliative Care

Pharmacy

OR/Anaesthesia

WardsChaplaincy

Baby/familyBaby/family

RTs

Social Work

DietaryRehab

PharmacyNPs

ChaplaincyPalliative Care

Lab & Blood BankLactation Consultants

CCU

ACTSCHN

CNNBioethics Dept

Palliative CareACTS

CCUCriticall

Public Health, CCACS Provincial Tertiary NICUs

Ontario perinatal partnershipp

The information environment

P t d t il d t ll tiPaper notes . . . detail enormous data collectionHand-annotated records of nursing staff, usually at 60 minute intervals . . orders of magnitude of data lossg

As many as 16 different streams of physiological data being displayed . . . rates ranging from one to 512

di / b d f 1 2 th ireadings/sec, observed for 1-2 months in some cases

Very common for critically ill babies to have significantly abnormal variation in the measuredsignificantly abnormal variation in the measured parameters minute by minute that are not recorded in the medical record

7

Signal AcquisitionSignal Acquisition

ECG

WeightBP

GlucoseA A

bUSN Hub

rtif

bstr

a c t

actiion

Bedside Device




Bedside Implementation

ECG

WeightBP

GlucoseA A

bUSN Hub

rtif

bstr

a c t

actiion

Bedside Device


ECG

WeightBP

GlucoseA A

bUSN Hub

rtif

bstr

a c t

actiion

Bedside Device


ECG

WeightBP

GlucoseAAA

USN Hub

WeightBP

Ar

bstr

Abst

Abst

Abst

Abst

ECGGlucose

USN Hub

tif a

racti

ract

ract

tract

trac

ECG

WeightBP

Glucose

USN

a c t

ion

ion

tion

tion

tionHub n


ECG

WeightBP

GlucoseAAA

USN Hub

WeightBP

Ar

bstr

Abst

Abst

Abst

Abst

ECGGlucose

USN Hub

tif a

racti

ract

ract

tract

trac

ECG

WeightBP

Glucose

USN

a c t

ion

ion

tion

tion

tionHub n

Percival, J., McGregor, C., Percival, N., Kamaleswaran, R., Tuuha, S., (2010), “A Framework for Nursing Documentation enabling Integration with EHR andReal-time Patient Monitoring”, 23rd IEEE International Symposium on Computer-Based Medical Systems, 468-73






Condition onset predictors

• Behaviour of physiological data streams that describe respiratory and cardiac function . . .

• Pneumothorax (McIntosh et al, 2000)• Nosocomial infection (Griffin and Moorman,

2001)2001)• Periventricular leucomalacia (Shankaran et al,

2006)2006)• Intraventricular haemorrhage (Fabres et al,

2006; Tuzcu et al 2009)2006; Tuzcu et al, 2009) 18

Motivation: Earlier Onset Detection

Absolute times 1/06

1/06

Onset Detection

B b 1Diagnosis

Absolute times

16/11

/

15/11

/

Baby 1

Baby 3

Baby 2Diagnosis

Diagnosis

Baby 3

Relative times

Baby 1

Baby 2

Diagnosis

Diagnosis

Diagnosis

Baby 3Diagnosis

19

MultidimensionalMultidimensional

Functional Agent Rules GeneratingProcessing Agent Temporal RelativeMulti-AgentData Mining

Functional Agent Rules Generating Agent

Processing Agent Temporal Agent

Relative Agent

Modelling Evaluation

ExtendedCRISP-DMModel

Data Understanding

Data PreparationDM Ruleset Generation

Select Significant

Ruleset

Formulate Null

Hypothesis

Run Statistical Processes

to test Hypothesis

C fi t D t

Load accepted Rule-sets into IDSS

H th i /R lTemporalTAMDDMFrameworkTasks

Local Collection and

clean up

Exploratory Data Mining across multiple streams

for multiple patients

Confirmatory Data Mining with Null

Hypothesis

Hypothesis/Rule generated and added

to the Rulebase

Temporal Abstraction- simple & complex

- multi stream

Relative Alignment

TemporalData

Warehouse

PhysiologicalData

Warehouse

ClinicalData

WarehouseRuleBase

Data Warehouse

TemporalRules

Relative Temporal

Data Relative

Rule

ClinicianClinician

Bjering H., McGregor, C., (2010), “A Multidimensional Temporal Abstractive Data Mining Framework”, Australasian Workshop On Health Informatics and Knowledge Mgmt, pp 29-38

ArtemisMP50

Babylog8000

ECGSpO2 BP HR

Artemis

Alert SinkOp

QRS

BP

RR PT

FAAR

SepsisBPA

EPHR Source Op

SpO2 Source Op

USE

R IN

T

MedicalDataHub

CapsuleTechServer

Babylog8000

Data Aquisition

Online Analysis ResultPresentation

InfoSphere Streams Runtime

BP

WT

SepsisBPA

WTABP Source Op

CIS Source Op

TE

RFA

CE

Hub

CIS Adapter

ConfigurationServer

ClinicalInformation

System

Data Integration MgrKnowledge Extraction

Data Miner HIRData MoverOntology Driven

Rule Modifier

Deployment Server

PatientStream

SPAD

E ID

E

Cognos

Knowledge Extraction

(Re)deployment Stream Persistency

E Knowledge Extraction

22

Data toKnowledge

Catley, C., Smith, K., McGregor C., James, A., Eklund, J.M., (2010), “A Framework to Model and Translate Clinical Rules to Support Complex Real-time Analysis of Physiological and Clinical Data”, 1st ACM International Health Informatics Symposium, 307-15

KnowledgeExtractionExtraction

Catley, C., Smith, K., McGregor C., James, A., Eklund, J.M., (2010), “A Framework to Model and Translate Clinical Rules to Support Complex Real-time Analysis of Physiological and Clinical Data”, 1st ACM International Health Informatics Symposium, 307-15




ArtemisMP50

Babylog8000

ECGSpO2 BP HR

Artemis

Alert SinkOp

QRS

BP

RR PT

FAAR

SepsisBPA

EPHR Source Op

SpO2 Source Op

USE

R IN

T

MedicalDataHub

CapsuleTechServer

Babylog8000

Data Aquisition



BP

WT

SepsisBPA

WTABP Source Op

CIS Source Op

TE

RFA

CE

Hub

CIS Adapter

ConfigurationServer

ClinicalInformation

System



Rule Modifier

Deployment Server

PatientStream

SPAD

E ID

E

Cognos




26

Apnoea

Catley, C., Smith, K., McGregor, C., James, A., & Eklund, J. M. (2011). “A Framework for Multidimensional Real-Time Data Analysis: A Case Study for the Detection of Apnoea of Prematurity”. International Journal of Computational Models and Algorithms in Medicine (IJCMAM), 2(1), 16-37

CentralApnoeaApnoea

Catley, C., Smith, K., McGregor, C., James, A., & Eklund, J. M. (2011). “A Framework for Multidimensional Real-Time Data Analysis: A Case Study for the Detection of Apnoea of Prematurity”. International Journal of Computational Models and Algorithms in Medicine (IJCMAM), 2(1), 16-37




ArtemisMP50

Babylog8000

ECGSpO2 BP HR

Artemis

Alert SinkOp

QRS

BP

RR PT

FAAR

SepsisBPA

EPHR Source Op

SpO2 Source Op

USE

R IN

T

MedicalDataHub

CapsuleTechServer

Babylog8000

Data Aquisition



BP

WT

SepsisBPA

WTABP Source Op

CIS Source Op

TE

RFA

CE

Hub

CIS Adapter

ConfigurationServer

ClinicalInformation

System



Rule Modifier

Deployment Server

PatientStream

SPAD

E ID

E

Cognos




30




CRISP-TDMn

http://www.crisp-dm.org/Process/index.htm

CRISP-TDMn




Innovation Translation inTranslation in

2011• Cost of Technology• Processing CapacityProcessing Capacity• Storage Capacity

Network Bandwidth Capacity• Network Bandwidth Capacity• Modularisation of Software• Trust of Solutions eg Not knowing how an

ANN making decisions•• Cost of QualityCost of Quality

Current Status of Artemis

• Deployed• Deployed August, 2009

• The Hospital for Sick Children, Toronto

• Maximum 8• Maximum 8 concurrent Neonatal ICU patients

• Enabling new NosocomialNosocomial Infection Clinical Researchhttp://www.youtube.com/watch?v=1s6xPy-IU4g

YouTube: IBM commercial data baby

ArtemisArtemis

• Upscaling to all• Upscaling to all patients within SickKids NICU

• 2 Hospitals Online

• Cloud Computing• Cloud Computing Version

• Other conditions and events

• Expand to other ICUs beyondICUs beyond Neonatal

http://www.youtube.com/watch?v=1s6xPy-IU4gYouTube: IBM commercial data baby

PaJMaPaJMa

Artemis CloudArtemis Cloud

Data IntegrationInfoSphere Streams Runtime

Artemis Cloud MonitorWeb

Service

DefineWeb

ServiceData IntegrationManager

Alert SinkOp

QRS

BP

RR PT

FA

WT

AR

SepsisBPA

EP

WTA

HR Source Op

SpO2 Source Op

BP Source Op

CIS Source Op

Patient

Stream


Clinical

PhysiologicalWeb

Service

ECG

SpO2

BP

HR

ECG

SpO2

BP

HR


TemporalData Miner

Data Mover

Ontology DrivenRule Modifier

Deployment Server

CIS Source Op

Patient

Stream

TA

WebService

TARules

PatientTAs

AnalyseWeb

ServiceHospital

Clinical RuleWeb

Service

McGregor, C., 2011, “A Cloud Computing Framework for Real-time Rural and Remote Service of Critical Care”, IEEE Computer Based Medical Systems, Bristol

Challenges and Roadmap f M hi L i ffor Machine Learning from

Medical Data StreamsMedical Data StreamsCarolyn McGregory g

Canada Research Chair in Health Informatics, ProfessorFaculty of Business and IT/Faculty of Health Sciencey y

University of Ontario Institute of [email protected]

Documents

Challenges and Roadmap fMhi L i ffor Machine Learning from Medical Data StreamsMedical ...users.med.up.pt/pprodrigues/lmds11/lemeds2011/McGr… · · 2011-10-24fMhi L i ffor Machine