机器学习预测患者住院风险，提升依从性可降三成住院率
The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses
- 用四种模型分析1171名患者数据，梯度提升表现最佳
- 高用药依从者住院风险降低38.3%，定期预防者降37.7%
- 适合医疗决策支持与个性化干预设计者参考
本研究揭示预防性护理和用药依从性在降低住院率中的关键作用。基于1,171名患者的结构化数据，采用逻辑回归、梯度提升、随机森林和人工神经网络四种机器学习模型预测五年内住院风险，其中梯度提升模型准确率达81.2%。结果显示，高用药依从性和持续预防护理可使住院风险分别降低38.3%和37.7%。研究还表明，针对性预防措施具有正向投资回报率（ROI），因此机器学习模型可有效指导个性化干预，带来长期医疗成本节约。
原文摘要 · Abstract (English)
This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.
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