Machine Learning-based Prediction of Unplanned Acute Care in Outpatients Receiving S-1 Chemotherapy
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND/AIM: The increasing use of oral anticancer agents in outpatient settings has led to a growing need for unplanned acute care (UAC) due to treatment-related adverse events. Early identification of high-risk patients is therefore clinically important. This study aimed to develop a machine learning-based model to predict UAC in outpatients receiving S-1 chemotherapy and to compare its performance with conventional logistic regression. PATIENTS AND METHODS: This retrospective single-center study included 579 outpatients who newly underwent S-1 therapy. UAC was defined as chemotherapy-related unplanned hospitalization or urgent outpatient visits during the first treatment course. Predictive models were developed using logistic regression and machine-learning algorithms, including a support vector machine (SVM). Model performance was evaluated using recall-oriented metrics, with the F2 score adopted as the primary performance measure. Shapley additive explanations (SHAP) were applied for feature selection and model interpretation. RESULTS: 0.325). CONCLUSION: An SVM-based machine-learning model improved the prediction of UAC among outpatients receiving S-1 chemotherapy by reducing false-negative predictions and may support early risk stratification to enhance the safety of outpatient chemotherapy.
Source: In Vivo
Authors: MISAKI TERAMOTO, TSUBURA NODA, KENJI KAWASUMI, MOMOKA FURUOKA, AYAKO MAEDA-MINAMI, YASUNARI MANO, MASASHI NAGATA, YOHEI KAWANO
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7166682002
FWCI:
Cites:
Cited by:
Related to: