The development of a scoring and ranking strategy for a patient-tailored adverse drug reaction prediction in polypharmacy.

ABSTRACT

Only few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy. The study aimed to develop a patient-tailored ADR web application, considering characteristics from 734 drugs and relevant patient related factors. The application was designed in Python using a scoring and ranking system based on frequency and severity, computed for each ADR and expressed through an online platform. A neural networks algorithm was used for predicting the severity of ADRs. The application inputs are age, gender, drugs, relevant pathologies. The outputs are an overall severity profile (hospitalization and mortality risk), a stratified risk on specific ADR groups and a sorted list of the most important ADRs depending on frequency and severity. The Severity prediction model validation resulted in 79.7-85.1% Area Under the Receiver Operating Characteristic Curve Score, which lies in the good cut-off of 75-90%. The program offers a complex view regarding the ADR profile of a given patient and could be used by the physician and clinical pharmacist during patient safety monitoring, for a coherent therapy choice or medication adjustment, due to the good therapy coverage and the inclusion of relevant patient comorbidities.