使用机器学习方法预测再入院的风险:对接受皮肤手术的患者的案例研究
Jigar Adhiya1, Behrad Barghi1, Nasibeh Azadeh-Fard1
1Industrial and Systems Engineering Department, Kate Gleason College of Engineering, Rochester Institute of Technology (RIT), Rochester, NY, United States.
Frontiers in artificial intelligence
|January 22, 2024
概括
机器学习模型,特别是XGBoost和随机森林,可以预测患者的再入院. 年龄,性别和治疗月份等因素会影响皮肤病患者的再入院风险.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 患者再接收分析
背景情况:
- 频繁的患者再入院在医疗保健中构成了重大挑战,增加了成本和患者的痛苦.
- 了解再接收的驱动因素对于改善患者的治疗结果和医疗保健效率至关重要.
研究的目的:
- 确定导致皮肤病治疗后患者再入院的关键因素.
- 评估各种机器学习算法在预测这些再录取方面的有效性.
主要方法:
- 对多个机器学习算法的比较分析,包括后勤回归,SVM,随机森林,天真贝叶斯学,ANN,XGBoost和KNN.
- 根据算法性能和数据模式,确定重新接收的重大预测因素.
主要成果:
- XGBoost和随机森林在预测患者再入院方面表现出卓越的准确性.
- 确定的风险因素包括男性性别,21-40岁年龄组,以及3月和4月发生的再录取.
- ~6%的患者在出院后一个月内重新入院.
结论:
- 机器学习模型,特别是XGBoost和Random Forest,为预测皮肤病患者再入院提供了一个有希望的方法.
- 患者年龄和治疗医院是重新入院可能性的重要决定因素,需要有针对性的干预措施.
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