Technically Representing Clinical Knowledge for Rehabilitation Care

Abstract

Providing a suitable rehabilitation after an acute episode or a chronic disease helps people to live independently and enhance their quality of life. However, the continuity of care is often interrupted in the transition from hospital to home. Virtual coaches (VCs) could help these patients to engage in personalized home rehabilitation programs. These coaching systems need also to be fed with procedural precepts in order to work as intended. This, in turn, relates both to properly represent the clinical knowledge (as the VC somehow replaces the formal caregivers that cannot be fully present) as well guide the patient correctly (in order to follow the medically desired procedures given the need for personalisation according to individual needs). Therefore, we outline our technical approach to deal with this. In particular, clinical pathways in terms of semi-formal procedure models in combination with machine learning components processing and powerful user interfaces providing these pathway information and feeding the VC are presented. The system is currently under testing in a participatory design phase called Living Lab. Thus, initial user feedback for further improvements is about to come.

Applying Goal-Oriented Modelling for Machine Learning Based Rehabilitation Care

“…This initial and general pathway information will be forwarded to a professional portal, where a clinician can personalize pathway parameters for each patient and enrich the FHIR resource with individual values. From this, a wrapper for clinical pathways is used to generate an instance of the underlying ontology, which will then be processed in order to personalize the initial patients' pathway with machine learning algorithms using context information detected from system components like sensor data [5]. In particular, the reinforcement learning algorithm, contextual bandits, is applied to personalize rehabilitation for the patient.…”