AI Insight
This study developed and validated machine learning models to identify patients with advanced chronic conditions (MACA) using electronic health records from a Spanish hospital. Among fourteen algorithms tested on a sample of 163 patients, a bagging classifier achieved the highest performance with 91% sensitivity and 0.90 AUC, using ten key predictors including dependency level, frailty, functional decline, and healthcare utilization patterns. Cross-validation demonstrated robust model stability with mean AUC values exceeding 0.95.
Why it matters
Early identification of MACA patients could enable more proactive and personalized healthcare interventions rather than relying on retrospective criteria or delayed clinical judgment. The machine learning approach provides an automated screening tool that could help healthcare systems better allocate resources and initiate timely care for vulnerable patient populations.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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 variables were extracted, including clinical, functional, and healthcare utilization indicators. Feature selection methods were applied, reducing the dataset to ten key predictors. Fourteen supervised classification algorithms were evaluated, including linear, probabilistic, and ensemble methods. Model performance was evaluated using various metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The final cohorts include 80 MACA patients and 83 controls. The bagging classifier achieved the most consistent performance with a sensitivity of 0.91 and an AUC of 0.90. Key predictors include absolute dependency, advanced frailty, functional decline, and healthcare utilization indicators. Cross-validation (CV) confirmed the stability of model performance, with mean AUC values exceeding 0.95. These findings highlight the potential of ML-based tools for early detection and the high discriminative capacity for identifying MACA patients.