分析意大利医院住院时长影响因素，用机器学习提升预测精度。
Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case
- 基于60多家医院数据，挖掘年龄、合并症等关键影响因素。
- CatBoost模型预测准确率最高，决定系数R2达0.49。
- 为医疗资源优化提供可解释的机器学习工具，适合医疗管理者参考。
住院时长是评估医疗质量与优化医院资源配置的关键指标。本研究聚焦意大利皮埃蒙特地区60余家医疗机构2020至2023年的住院记录，探索患者特征、共病情况、入院详情及医院属性等多维度因素对住院时长（LoS）的影响。研究发现年龄组、共病评分、入院类型及入院月份与住院时长存在显著相关性。采用CatBoost和随机森林等机器学习模型进行预测，其中CatBoost表现最佳，获得最高R2值0.49，表明模型具备良好预测能力。
原文摘要 · Abstract (English)
Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identify factors influencing LoS within the Italian healthcare context, using a dataset of hospitalization records from over 60 healthcare facilities in the Piedmont region, spanning from 2020 to 2023. We explored a variety of features, including patient characteristics, comorbidities, admission details, and hospital-specific factors. Significant correlations were found between LoS and features such as age group, comorbidity score, admission type, and the month of admission. Machine learning models, specifically CatBoost and Random Forest, were used to predict LoS. The highest R2 score, 0.49, was achieved with CatBoost, demonstrating good predictive performance.
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