Introducing the TOP framework: a novel phenotyping solution for collaborative phenotype algorithm development and application

Abstract Background Phenotyping, the comprehensive assessment of observable characteristics, is essential for advancing medical understanding and personalised healthcare. However, traditional phenotyping methods are often manual, time-intensive, and limited in scope. To address these challenges, this work introduces the TOP Framework, a software suite that leverages a structured approach. It provides tools for the formal definition of phenotypes, their organisation into ontological classes, the creation of phenotype models for disease-specific knowledge representation, and the generation of phenotype queries for automated data retrieval and analysis. A dynamic phenotype algorithm integrates these modules to efficiently identify individuals meeting complex phenotypic criteria. The Model for End-Stage Liver Disease (MELD) score serves as a running example to illustrate the framework’s capabilities. Furthermore, this paper presents a preliminary evaluation of the TOP Framework’s user experience by means of the User Experience Questionnaire (UEQ), assessing its usability and suitability for researchers and clinicians. Results The TOP Framework includes a robust implementation of a reasoner model for deriving complex phenotypes and an automated testing module to ensure reliability. The user experience evaluation yielded generally positive results on a scale from −3 to 3 ( n = 11), with mean scores and 95% confidence intervals as follows: attractiveness, 1.50 (CI = (1.07, 1.93)); pragmatic quality, 1.38 (CI = (1.00, 1.77)); and hedonic quality, 1.40 (CI = (0.76, 2.03)). Conclusions The TOP Framework offers a novel and automated approach to phenotyping, with the potential to enhance the efficiency, scalability, and reproducibility of phenotyping studies. This advancement contributes to a deeper understanding of disease and the progression of precision medicine.