使用机器学习的肺癌患者的急诊室访问的风险预测:回顾性观察研究
Ah Ra Lee1, Hojoon Park1, Aram Yoo1
1Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
JMIR medical informatics
|December 6, 2023
概括
机器学习可以预测肺癌患者的急诊室访问. 最近的紧急访问和实验室结果是关键预测因素,有助于早期干预和资源管理.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 肺癌患者经常因癌症相关问题访问急诊室,往往预后不佳.
- 使用常规临床数据预测ED访问可以改善资源分配和患者的治疗结果.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测肺癌患者的急诊室访问.
- 确定与这些急诊室访问相关的关键临床风险因素.
主要方法:
- 在2010-2017年间诊断的肺癌患者的回顾性观察性研究.
- 使用常用数据模型开发一个ML预测模型.
- 分析特征的重要性,以确定重要的临床预测因素.
主要成果:
- 性能最好的ML模型在接收器操作特征曲线下的面积达到0.73.
- 最近频繁的急诊室访问和特定的实验室测试结果被确定为重要的预测因素.
- 该模型有效地预测了肺癌患者的急诊室出勤率.
结论:
- 一个基于ML的风险预测模型已成功开发,用于肺癌患者的急诊室访问.
- 该模型有助于识别高风险患者进行早期干预,提高医疗保健效率和质量.
- 该研究强调了共同数据模型的潜力,用于跨机构的肺癌协作精准医学研究.
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