Evaluating the Development of a Machine Learning Model for Predicting Length of Stay for Inpatients in a Tertiary General Hospital

초록

Purpose: This study aimed to develop a machine learning (ML)-based predictive model for hospital length of stay incorporating clinical, nursing, and healthcare system factors to optimize hospital resource allocation, improve patient-centered care, and enhance nursing workflow efficiency. Methods: This retrospective study analyzed a large dataset of inpatient electronic medical records from a private tertiary hospital. The dataset was used to develop predictive models for long-term versus short-term hospitalization. The modeling process involved several ML algorithms, and their performance was evaluated using standard statistical metrics. The most significant predictive variables were identified through an analysis of their feature importance. Results: Among the tested models, the Random Forest algorithm exhibited the highest predictive accuracy, demonstrating strong performance in predicting hospital length of stay. Key influencing factors included the number of consultations, postoperative recovery time, duration of stay in the intensive care unit, the use of third-generation antibiotics, and the need for infection isolation. Patients requiring ventilator care, intensive care unit admission, and specific powerful antibiotics were more likely to experience prolonged hospitalization. Additionally, nursing-related factors such as fall risk and pressure ulcer risk were significantly correlated with an extended hospital stay. Conclusion: This study demonstrates that ML models can effectively predict hospital length of stay, aiding in hospital resource management, nursing workforce allocation, and patient safety interventions. The integration of predictive analytics into healthcare systems can support early risk assessment, personalized discharge planning, and overall hospital efficiency. © 2026

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