Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.

ABSTRACT: Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms-XGBoost, Elastic Net logistic regression, and an Artificial Neural Network-to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000

SUBMITTER: Hernandez-Arango A 

PROVIDER: S-EPMC11790785 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature