Ricerc@Sapienza

In healthcare daily practice, clinical decisions (e.g., choice of drug and dosage patterns, timing of measurements, etc.) are taken as a consequence of interviews, visual inspections, and clinical assays (e.g., blood samples, RMIs, etc.) on the patient, and aim at achieving given treatment goals, while safeguarding patient¿s health from the possible adverse effects of the chosen treatment. From a control engineering point of view, the physician acts as a controller on the patient (who acts as the plant in a feedback control-loop).

As a result of the standardisation of the clinical workflow, clinical practice has shown that treatment protocols and guidelines improve healthcare quality, and help to avoid risks (for example those related to overlooking routine checks).
Rules prescribed by clinical guidelines, however, are mainly qualitative suggestions for clinical actions that have to be taken facing a concrete clinical situation.

The project aims at supporting the modelling and formalisation of treatment strategies and best practices to be offered within Clinical Decision Support Systems (CDSSs) for different healthcare domains, by using a hybrid approach which combines model checking, gamification, machine learning, active learning, and knowledge engineering techniques.

Namely, the project goal is to design novel general methods and software (hence customisable to several areas of medicine) to:
- Learn core decision strategies from retrospective data from Electronic Health Records (EHRs)
- Acquire and continuously peer-review medical expert knowledge through user-engaging serious games
- Continuously improve the acquired decision strategies by actively learning from intelligently-chosen synthetic medical cases submitted to experts for evaluation.

Thanks to our interdisciplinary team, we plan to evaluate the effectiveness of our methods and software in two areas of psychiatry: mood disorders during pregnancy and forensic psychiatric evaluations.