biostudies-literatureUnknown12Masiero M<h4>Background</h4>Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant.<h4>Objective</h4>The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patientJMIR research protocolse48852https://www.ebi.ac.uk/biostudies/studies/S-EPMC10755656biostudies-literatureA Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.PMC10755656Masiero MSpada GEPietrobon RPravettoni GFragale ETeixeira LValencia MMachiavelli AMunzone EPezzolato MSanchini VfalseA Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.<h4>Background</h4>Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant.<h4>Objective</h4>The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patient2023-01-01T00:00:00Z2023 Dec2025-04-22T05:36:44.356Z2024-11-14T07:29:25.609ZS-EPMC107556563809600210.2196/48852