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

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.