Embed Size (px)
Citation preview
Supporting clinical professionals in the decision-making for
patients with chronic diseases
Mitja Luštrek1,Božidara Cvetković1, Maurizio Bordone2, Eduardo Soudah2,
Carlos Cavero3, Juan Mario Rodríguez3, Aitor Moreno4, Alexander Brasaola4, Paolo Emilio Puddu5
1 Jožef Stefan Institute, Slovenia2 CIMNE, Spain3 Atos, Spain4 Ibermática, Spain5 University of Rome “La Sapienza”, Italy
Rationale
• Medical labs produce a lot of data on a patient• Telemonitoring produces even more data • The amount of medical literature is huge• Overwhelming for a clinical professional
Rationale
• Medical labs produce a lot of data on a patient• Telemonitoring produces even more data • The amount of medical literature is huge• Overwhelming for a clinical professional
• Needs tools to make sense of all these data• Decision support system (DSS)
Clinical workflow
1. The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.
Clinical workflow
1. The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.
2. The doctor examines the patient’s current (and historic) risk, computed by the DSS.
Clinical workflow
1. The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.
2. The doctor examines the patient’s current (and historic) risk, computed by the DSS.
3. If the risk is high, the doctor looks for reasons. The DSS computes the contribution to the risk for each of the monitored parameters.
Clinical workflow
1. The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.
2. The doctor examines the patient’s current (and historic) risk, computed by the DSS.
3. If the risk is high, the doctor looks for reasons. The DSS computes the contribution to the risk for each of the monitored parameters.
4. The doctor may look for further information in the medical literature with the help of the DSS.
Clinical workflow
1. The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.
2. The doctor examines the patient’s current (and historic) risk, computed by the DSS.
3. If the risk is high, the doctor looks for reasons. The DSS computes the contribution to the risk for each of the monitored parameters.
4. The doctor may look for further information in the medical literature with the help of the DSS.
5. The doctor may reconfigure the DSS.
DSS architecture
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Risk assessment – expert knowledge
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Monitored parameters
• Search of medical literature for parameters affecting the risk (for congestive heart failure)
• Survey among 32 cardiologists to determine the importance of these parameters
Monitored parameters
• Search of medical literature for parameters affecting the risk (for congestive heart failure)
• Survey among 32 cardiologists to determine the importance of these parameters
• Additional information for each parameter:– Minimum, maximum value– Whether larger value means higher or lower risk– Values indicating green, yellow or red condition– Frequency of measurement (low = static, medium =
measured by the doctor, high = telemonitored)
Risk assessment models
• Normalize parameter values: [0, 1] interval, 0 = lowest risk, 1 = highest risk
Risk assessment models
• Normalize parameter values: [0, 1] interval, 0 = lowest risk, 1 = highest risk
• Long-term model: sum of normalized values, weighted by their importance
• Medium-term model: low-frequency parameters weighted by 1/3
• Short-term model: low-frequency parameters weighted by 1/9, medium-term by 1/3
Prototype
Risk assessment – machine learning
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Risk assessment – machine learning
1. Training data:[parameter values, cardiac event or no event]
2. Feature selection, decorrelation3. Machine learning model selection:
multilayer perceptron with input (parameters), hidden, and output (risk) layer
4. Training:85 % accuracy on a public heart disease dataset
Risk assessment – anomaly detection
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Risk assessment – anomaly detection
Detect anomalous (= not observed before) parameter values and their relations
+ No knowledge or data labeled with cardiac events needed
– Anomalies do not alway mean higher risk
Risk assessment – anomaly detection
Detect anomalous (= not observed before) parameter values and their relations
+ No knowledge or data labeled with cardiac events needed
– Anomalies do not alway mean higher risk
More on this in a separate presentation in this session by Božidara Cvetković
Literature consultation
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Literature consultation
Free text / PICO question Query
Free text (EHR) contextualization
Ontology maping
Semantic search
Resources:PubMedCochrane Library...
Results
Annotate, evaluate
Ranking
Alerts and configuration
Electronichealthrecord
Sensors
Literature consultationExternaldata
Risk assessment
Expertknowledge
Machinelearning
Anomalydetection
Alerts Configuration
Alerts and configuration
Alerts:• Rule engine using the Drools platform• Rules triggered on parameter or risk values• Alert modes (SMS, email) depend on the trigger
Alerts and configuration
Alerts:• Rule engine using the Drools platform• Rules triggered on parameter or risk values• Alert modes (SMS, email) depend on the trigger
Configuration:• Parameters to be monitored for each patient• Parameter values indicating green, yellow or red
condition for each patient
Conclusion
• DSS tailored to a (fairly generic) clinical workflow
• Can be used for all diseases to which the workflow is applicable
• Congestive heart failure as a case study
Conclusion
• DSS tailored to a (fairly generic) clinical workflow
• Can be used for all diseases to which the workflow is applicable
• Congestive heart failure as a case study• Observational study with 100 patients starting
shortly• Tuning and testing once the data from the study
is available
