预测癌症住院患者因心血管疾病而导致的意外再入院:一种机器学习方法
Sola Han1, Ted J Sohn1, Boon Peng Ng2,3
1Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA.
Scientific reports
|August 18, 2023
概括
机器学习准确地预测癌症患者的心血管疾病 (CVD) 再入院. 这有助于识别高风险个体,可能减少昂贵的意外住院治疗,改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 复杂化了癌症护理,增加了计划外的再入院,死亡率和成本.
- 在癌症患者中预测心血管疾病相关的再入院对于主动管理和资源分配至关重要.
研究的目的:
- 评估机器学习 (ML) 模型在预测住院癌症患者因心血管疾病而导致的180天计划外再入院的可行性.
- 在这个人群中确定与心血管疾病相关的无计划再接收的关键预测因素.
主要方法:
- 利用了2017-2018年全国再接收数据库,包括住院癌症患者.
- 实现并比较了决策树 (DT),随机森林,XGBoost和AdaBoost ML算法.
- 评估模型性能使用准确度,精度,回忆,F2分数和AUC.
主要成果:
- 在358,629名癌症患者中,5.86%的患者在180天内经历了与心血管疾病相关的无计划再入院.
- 集合算法,特别是XGBoost,表现优于决策树模型.
- 重要的预测因素包括住院时间,患者年龄和接受癌症手术.
结论:
- 机器学习模型在预测癌症患者中与心血管疾病相关的无计划再入院方面证明了可行性.
- XGBoost表现强,为风险分层提供了有价值的工具.
- 识别关键预测因素可以告知有针对性的干预措施,以减少再接收率.
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