Drug grouping learning for improving evidence-based treatment recommendations

Abstract

Clinical practice guidelines (CPGs) are essential tools that facilitate the translation of the growing body of scientific evidence into clinical practice by providing clinicians with evidence-based recommendations. The first step of CPG development is the formulation of a clinical question involving an intervention of interest. For some interventions, the quantity and quality of the available scientific evidence can vary. This can significantly impact the treatment recommendations. In this work, we present a method for formulating clinical questions involving pharmacological interventions by considering groups of drugs with shared characteristics. This work focuses on drug grouping based on the treatment outcomes desired by both patient and clinician in addition to pharmacological features. To that end, a new method has been presented to learn distances among drugs that is personalized by considering the preferences of users, and an ensemble clustering method is designed to identify the most suitable grouping for each query. We demonstrate our approach in the context of attention deficit hyperactivity disorder (ADHD). Results demonstrate the feasibility of the approach

This work received joint funding from the European Regional Development Fund (ERDF), the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO) and the Carlos III Research Institute, under grant nos. PI19/00375 and PI23/01690. This work received support from the Generalitat de Catalunya 2021 SGR 01125

Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier

3