Intelligent Medicine · Published 2025-09-17 · DOI 10.1016/j.imed.2025.08.006
Background Patients who experience acute hospitalization face a risk of suffering adverse events, such as delirium, pressure ulcers, or pain. This risk gets aggravated in individuals with multimorbidity. Furthermore, the prevalence of multimorbidity is notably high, and gets even higher for elder people. In addition, the interaction between multiple adverse events can significantly impact mortality. Previous efforts to predict this kind of events have not produced satisfactory results, particularly for older patients with multimorbidity in emergency room settings. Having a clinical prediction rule (CPR) that can accurately predict adverse events in this population is crucial to prevent these events and improve patient outcomes.Methods This study enrolled patients with multimorbidity who were admitted to an acute care unit from December 2021 to June 2023. The dimensionality of this dataset was reduced from 43 to 10 features through the implementation of a normalization-based ensemble technique, integrating feature selection methods from different categories: filter methods, wrapper methods, and embedded models to ensure robust validation. A stratified k-fold cross-validation was applied to reduce the risk of overfitting caused by the imbalanced distribution of the data set. Once the relevant predictors were identified, the sequential forward selection (SFS) technique was used to determine the optimal subsets of predictors that maximize model accuracy.Results The evaluation of the performance of these subsets using different classification algorithms led to the development of a CPR using only the three most relevant predictors. The metrics of different models were compared, and the support vector machine (SVM) model was selected due to its superior area under curve (AUC)-receiver operator characteristic (ROC) (0.93) and better handling of class unbalancing and rest of parameters (accuracy 0.91, precision and recall 0.83, and specificity 0.94). To facilitate the application of this prediction rule, a web application that streamlines the detection, classification, and prediction processes of these outcomes was developed.Conclusion The proposed model may achieve high accuracy and stability by requiring fever events to predictadverse outcomes in patients with multimorbidity in emergency settings compared with conventional methods.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →