在心胸外科手术中预测出院后的并发症:一个临床决策支持系统,以优化远程患者监测资源
Ricardo Santos1, Bruno Ribeiro2, Inês Sousa2
1Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal; Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UNL), Physics Department, NOVA School of Science and Technology, 2829-516 Caparica, Portugal.
International journal of medical informatics
|December 7, 2023
概括
这项研究开发了一种机器学习模型,用于识别心脏手术患者,这些患者有高风险出现出院后的并发症. 这使得有针对性的远程患者监测,优化资源配置和改善患者的结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心胸外科手术的结果
背景情况:
- 在心脏手术患者中,出院后的并发症很常见.
- 远程患者监控 (RPM) 可以减轻这些风险,但面临着资源限制.
- 现有的风险模型不能充分预测未来的不良事件在释放后.
研究的目的:
- 开发基于机器学习 (ML) 的临床决策支持系统 (CDSS).
- 估计心胸外科手术患者在出院后90天内出现严重并发症的风险.
- 为了优化有限的RPM资源的分配.
主要方法:
- 在十年内利用了5045名心胸外科手术患者的健康记录.
- 训练有素的预测模型结合临床医生的见解和AI要求.
- 使用两个不同的测试集和模拟的真实世界RPM资源分配来评估模型的概括性.
主要成果:
- 在一组测试中达到收发器运行特征曲线下的面积为69.5%,在另一组测试中达到65.3%.
- 证明根据模型的RPM风险得分来优先考虑患者比随机分配更有利.
- 拟议的模型成功地确定了更多的RPM高风险患者.
结论:
- 开发的基于ML的CDSS可以有效地识别心胸外科手术后出院后并发症的高危患者.
- 该系统有助于有效地分配有限的RPM资源.
- 实施这种CDSS可以改善患者管理,并可能减少不良结果.
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