一项关于"违背医疗建议"急诊室患者的研究:一个优化的可解释的人工智能框架
Abdulaziz Ahmed1,2, Khalid Y Aram3, Salih Tutun4
1Department of Health Services Administration, School of Health Professions, The University of Alabama at Birmingham, Birmingham, AL, 35233, USA. aahmed2@uab.edu.
Health care management science
|August 14, 2024
概括
了解为什么患者违背医疗建议 (LAMA) 离开急诊室至关重要. 本研究介绍了一个使用机器学习来预测LAMA结果的框架,有助于减少不良事件.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 患者安全 患者安全
背景情况:
- 违背医疗建议离开 (LAMA) 是急诊室 (ED) 的一个常见问题.
- 拉马带来医学法律风险,包括再接收,死亡率和收入损失.
- 确定影响LAMA的因素对于减轻不良结果至关重要.
研究的目的:
- 提出一个新的框架来研究影响EDsLAMA的因素.
- 开发和评估LAMA结果的预测模型.
- 为了提高患者的安全性和减少与LAMA相关的ED风险.
主要方法:
- 机器学习,元启发优化和模型解释的整合.
- 使用自适应式塔布模拟化 (ATSA) 算法进行极端梯度增强 (XGB) 超参数优化.
- 使用SHaply添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- 优化的XGB模型显示了对LAMA结果的预测能力.
- 最好的模型实现了曲线下面积 (AUC) 的76%和82%的灵敏度.
- SHAP分析提供了对驱动模型预测因素的见解.
结论:
- 拟议的框架有效地预测ED环境中的LAMA结果.
- 机器学习和优化技术可以成为解决LAMA的宝贵工具.
- 可解释的人工智能模型可以帮助理解和减轻与患者违背医疗建议离开相关的风险.
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