开发和验证与临床工作流程集成的机器学习模型,用于预测住院患者出院日期
Mohammed A Mahyoub1,2, Kacie Dougherty1, Ravi R Yadav1
1Advanced Analytics and Solutions, Virtua Health, Marlton, NJ, United States.
Frontiers in digital health
|October 15, 2024
概括
使用机器学习准确预测出院日期,提高了医院的效率. 开发的模型提高了预测准确度高达35.68%,减少了18.96%的过度住院日.
科学领域:
- 医疗保健管理的管理
- 机器学习在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 预测出院日期对于有效的医疗保健资源分配和患者护理计划至关重要.
- 准确的预测优化了医院的运作,并改善了患者的治疗结果.
研究的目的:
- 开发和实施一个强大的排放日期预测模型.
- 利用临床专业知识和先进的机器学习来提高预测准确度.
主要方法:
- 与临床专家合作,确定关键数据元素.
- 在结构化和非结构化电子医疗记录 (EMR) 数据上利用特征工程.
- 使用XGBoost机器学习算法进行预测,并将其集成到EMR系统中.
主要成果:
- 与基线估计相比,F1得分的性能提高了高达35.68%.
- 部署后的经验证明的操作价值,与MS GMLOS保持一致.
- 有助于减少18.96%的多余住院日.
结论:
- 开发的出院日期预测模型在临床实践中显示出显著的有效性和潜在价值.
- 改进出院日期估计可以提高医疗保健资源管理和患者护理规划.
- 进一步的研究应侧重于长期适用性和对患者结果的影响.
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