1 Jožef Stefan Institute, Slovenia 2 CIMNE, Spain 3 Atos, Spain 4 Ibermática, Spain

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


2. The doctor examines the patient’s current (and historic) risk, computed by the DSS.

Clinical workflow



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




4. The doctor may look for further information in the medical literature with the help of the DSS.

Clinical workflow




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


Sensors


Risk assessment

Expertknowledge

Machinelearning

Anomalydetection


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


Sensors


Risk assessment

Expertknowledge

Machinelearning

Anomalydetection


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


Sensors


Risk assessment

Expertknowledge

Machinelearning

Anomalydetection



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


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


Sensors


Risk assessment

Expertknowledge

Machinelearning

Anomalydetection


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


Sensors


Risk assessment

Expertknowledge

Machinelearning

Anomalydetection



Alerts:• Rule engine using the Drools platform• Rules triggered on parameter or risk values• Alert modes (SMS, email) depend on the trigger


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

1 Jožef Stefan Institute, Slovenia 2 CIMNE, Spain 3 Atos, Spain 4 Ibermática, Spain

Documents

Transcript of 1 Jožef Stefan Institute, Slovenia 2 CIMNE, Spain 3 Atos, Spain 4 Ibermática, Spain