超越死亡率预测:初级保健医生对患者息治疗需求的看法,由机器学习工具预测
Lisa Rotenstein1,2, Liqin Wang1,3, Sophia N Zupanc2,4
1Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Applied clinical informatics
|April 18, 2024
概括
一个机器学习工具有效地识别需要严重疾病对话 (SIC) 和息治疗的患者. 该工具与初级保健医生达成了实质性的协议,有助于对高风险患者的临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 抚慰性护理医学 抚慰性护理医学
- 主要护理是指初级护理.
背景情况:
- 识别需要严重疾病对话 (SIC) 和息治疗的患者对于改善结果至关重要.
- 机器学习 (ML) 工具在临床实践中为风险分层提供了潜力.
研究的目的:
- 评估初级保健医生 (PCP) 对SIC或息护理干预的需要的看法.
- 评估ML死亡率预测工具与PCP之间关于患者风险和干预需求的一致性.
主要方法:
- 在四个初级保健站点对PCP进行了一项调查.
- 一组ML算法预测了成年患者1年死亡风险.
- 对ML风险评估视而不见的PCP评估了患者对息治疗需求的评估.
主要成果:
- ML工具标记了51.9%的患者为高风险的一年死亡率.
- PCP和ML工具在SIC.的需求上显示出实质性的共识 (Gwet的系数=0.640).
- 对于工具认为高风险且需要SIC的患者,一致性为68.7%,对于那些被认为低风险且不需要SIC的患者,一致性为79.1%.
结论:
- 基于ML的死亡率预测工具显示出作为临床决策支持系统的前景.
- 这些工具可以帮助临床医生识别那些可以从息护理干预中受益的患者.
- 该研究强调了ML的潜力,以提高及时提供息护理的可能性.
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