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Supporting clinical professionals in the decision-making for patients with chronic diseases Mitja Luštrek 1, Božidara Cvetković 1, Maurizio Bordone 2,

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Presentation on theme: "Supporting clinical professionals in the decision-making for patients with chronic diseases Mitja Luštrek 1, Božidara Cvetković 1, Maurizio Bordone 2,"— Presentation transcript:

1 Supporting clinical professionals in the decision-making for patients with chronic diseases Mitja Luštrek 1, Božidara Cvetković 1, Maurizio Bordone 2, Eduardo Soudah 2, Carlos Cavero 3, Juan Mario Rodríguez 3, Aitor Moreno 4, Alexander Brasaola 4, Paolo Emilio Puddu 5 1 Jožef Stefan Institute, Slovenia 2 CIMNE, Spain 3 Atos, Spain 4 Ibermática, Spain 5 University of Rome “La Sapienza”, Italy

2 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

3 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)

4 Clinical workflow 1.The doctor starts examining the condition of a patient, possibly because of an alert by the DSS.

5 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.

6 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.

7 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.

8 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.

9 DSS architecture Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

10 Risk assessment – expert knowledge Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

11 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

12 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)

13 Risk assessment models Normalize parameter values: [0, 1] interval, 0 = lowest risk, 1 = highest risk

14 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

15 Prototype

16 Risk assessment – machine learning Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

17 Risk assessment – machine learning 1.Training data: [parameter values, cardiac event or no event] 2.Feature selection, decorrelation 3.Machine learning model selection: multilayer perceptron with input (parameters), hidden, and output (risk) layer 4.Training: 85 % accuracy on a public heart disease dataset

18 Risk assessment – anomaly detection Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

19 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

20 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

21 Literature consultation Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

22 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

23 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

24 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

25 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

26 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

27 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

28 Literature consultation Free text / PICO question Query Free text (EHR) contextualization Ontology maping Semantic search Resources: PubMed Cochrane Library... Results Annotate, evaluate Ranking

29 Alerts and configuration Electronic health record Sensors Literature consultation External data Risk assessment Expert knowledge Machine learning Anomaly detection Alerts Configuration

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

31 Alerts and configuration Alerts: Rule engine using the Drools platform Rules triggered on parameter or risk values Alert modes (SMS, ) depend on the trigger Configuration: Parameters to be monitored for each patient Parameter values indicating green, yellow or red condition for each patient

32 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

33 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


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