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在一个学术医院进行的神经外科再接收减少计划,利用机器学习,工作流分析和模拟.

Tzu-Chun Wu1,2, Abraham Kim1,2,3, Ching-Tzu Tsai1,2

  • 1Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.

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科学领域:

  • 神经外科 神经外科
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 预测30天的住院再接收对于患者护理和资源管理至关重要.
  • 现有的神经外科再入院机器学习 (ML) 模型缺乏临床实施细节.
  • 这项研究解决了在神经外科再接收预测中需要实用,可实施的ML解决方案的需求.

研究的目的:

  • 开发高性能ML模型 (AUROC>0.8) 以预测30天的神经外科再入院.
  • 通过临床工作流分析和采访,确定可行的干预措施.
  • 模拟实施这些预测模型和干预措施的临床和财务影响.

主要方法:

  • 使用电子健康记录和五种ML方法:梯度提升,决策树,随机森林,脊梁逻辑回归和线性支向量机.
  • 进行半结构面试,以确定在手术前,住院,出院和后续阶段的干预点.
  • 应用校准剂型模型 (ABM) 来模拟再接收率和降低成本.

主要成果:

  • 随机森林模型在神经外科重症监护室 (NSICU) 的入院中实现了0.89的AUROC.
  • 确定了六项干预措施,针对患者护理的关键阶段.
  • 模拟结果显示,再接收率大幅降低 (例如,NSICU从13.13%降至10.12%),预计节省超过130万美元.

结论:

  • 成功开发并模拟了一种基于ML的方法,用于预测和减少神经外科手术中的30天再入院.
  • 建议的干预措施证明了改善患者结果和减轻财务损失的可行性.