Early identification of advanced chronicity (MACA) patients using Machine Learning models: a population-based predictive approach for proactive care stratification

Early identification of patients with advanced chronic conditions (MACA) remains a critical challenge in clinical practice, often relying on retrospective criteria or clinical judgment, which may delay timely and personalized intervention. The increasing availability of electronic health records (EHR) enables the application of Machine Learning (ML) techniques to support more proactive detection. This study aimed to develop and internally validate a ML-based approach for the identification of MACA patients using collected data from Hospital Universitario Parc Tauli (Sabadell, Spain). A retrospective observational study was conducted using a sample of 163 patients. A total of 80 candidate var