Efficient Explainable and Evidence Based Precision and Personalisation of Treatment - Wickramasinghe, Nilmini; Ulapane, Nalika; Ranaut, Anamika; Cohen, Neale (39th Bled eConference: Co-Creating Human-Centred and Responsible Digital Futures; Conference Proceedings, 2026)
Modern advances in computation enable the use of complex machine learning algorithms and artificial intelligence to assist human decision-making. However, the lack of explainability entailing from the black box nature of complex machine learning algorithms, inhibit their adoption in real-world applications especially in fields like healthcare. To address this challenge, we explored using the Odds Ratio (OR)—a clinically well-known measure of evidence—coupled with a sorting algorithm to prototype an explainable clinical decision support system (CDSS). This CDSS intakes relevant patient information such as demographic variables, clinical variables, medical history, and so on, and ranks treatment options personalised for patients, based on OR evidence. We present in this work-in-progress paper how our algorithm performs personalised ranking of therapies, taking Type-2 diabetes as a case study. As future work, we endeavour to codesign this further with clinicians to produce a primary care CDSS and assess long-term clinical outcomes.