Abstract
Bipolar disorder is a condition characterised by episodes of extreme mood fluctuation. Effective treatment typically includes psychoeducation, which aims to equip patients with the knowledge needed to make informed decisions that can influence the course of their illness. Key to this process is the ability to monitor the patient’s behaviour (both by patients themselves and involved healthcare specialists) in order to recognise early signs of future episodes. To support this monitoring task, we propose a framework grounded in medical ontologies and metric interval temporal logic (MITL). The framework enables monitoring of patient behaviour and the identification of emerging episodes based on behavioral patterns and established clinical guidelines. This work is the abstract of a paper [1] accepted at the Semantic Technologies for Data Management workshop (ST4DM 2026), outlining the formal foundations of the framework and describes future steps towards its adoption in practice.