Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population - 26/06/24
Abstract |
Background |
Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments.
Methods |
This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision.
Results |
This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors.
Conclusions |
Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.
Le texte complet de cet article est disponible en PDF.Keywords : Hospital readmissions, Machine learning, Natural language processing
Plan
Funding: None. |
|
Conflict of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
|
Authorship: All authors read and approved the manuscript. RL: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Datacuration, Conceptualization. AB-Y: Writing – review &editing. SR: Writing – review & editing, Supervision, Conceptualization. YT: Writing – review &editing, Supervision, Conceptualization. |
|
Data Availability: All relevant data are available in the article/dedicated git repository. Due to privacy and other restrictions, the primary data cannot be made openly available. The models are accessible upon request for academic purposes only. |
Vol 137 - N° 7
P. 617-628 - juillet 2024 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?