challenges and roadmap fmhi l i ffor machine learning from medical data streamsmedical...
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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
• 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
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
• 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
Bedside Implementation
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
Bedside Implementation
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
Bedside Implementation
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
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
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
• 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
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
• 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
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
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
• 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
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
30
• 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
CRISP-TDMn
http://www.crisp-dm.org/Process/index.htm
CRISP-TDMn
• 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
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
InfoSphere Streams Runtime
Clinical
PhysiologicalWeb
Service
ECG
SpO2
BP
HR
ECG
SpO2
BP
HR
Knowledge Extraction
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]