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The adoption of electronic health records has considerably enhanced access to large volumes of clinical data. While this accessibility is invaluable for both healthcare delivery and research, it also introduces new challenges arising from the complexity of medical data. These challenges include its implicitness (i.e., the need for domain expertise to interpret data), imperfections such as inconsistency, uncertainty, and incompleteness , and its inherently temporal nature. This thesis investigates how logic-based approaches can address these challenges.
First, I investigated an ontology-driven approach to illustrate how ontologies can be used to evaluate medical data quality, with a focus on lung cancer phenotyping. This involved designing an ontology to capture essential domain knowledge and applying it to query the Clinical Data Warehouse of Bordeaux University Hospital. The work highlighted both the benefits of ontologies in representing domain knowledge and identifying inconsistencies, as well as their limitations, particularly in handling temporally inconsistent healthcare data.
Building on this experience, I then proposed a novel logic-based framework for inferring high-level events from temporal clinical data, in a way that better aligns with clinical reasoning and decision-making . The framework defines logical rules specifying the existence conditions of an event at a given time-point, along with optional termination conditions that signal its possible end. It also introduces two aggregation methods to construct event intervals from these conditions. Furthermore, the formalism supports the definition of meta-events, obtained by combining or generalizing other events, and integrates confidence levels and a repair mechanism to handle imperfections in event detection. To validate the framework, I implemented its core components using Answer Set Programming, a declarative logic programming paradigm, and evaluated the resulting system, CASPER, on two medical use cases. The evaluation showed both computational feasibility and alignment with expert medical opinions.

 

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