预测分析用于早期检测医院获得的并发症:一种人工智能方法
Syed Aqif Mukhtar1,2, Benjamin R McFadden3, Md Tauhidul Islam4
1Government of Western Australia, Australia.
概括
机器学习模型准确地预测了医院获得的并发症 (HAC). 后勤回归模型显示出最佳性能,使实时风险评估能够提高患者安全和资源配置.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 住院并发症 (HACs) 对患者的康复产生负面影响,并增加医疗保健成本.
- 有效的风险预测对于减轻HAC和改善患者结果至关重要.
研究的目的:
- 开发一种机器学习 (ML) 框架,利用医院行政数据预测HAC风险.
- 为西澳大利亚北大都会卫生服务 (NMHS) 中的患者创建特定站点的预测算法.
主要方法:
- 在NMHS中对64,315名患者 (2020年7月至2022年6月) 的回顾性队列研究.
- 从270个变量中选择特征,其中37个包含在ML模型中 (逻辑回归,决策树,随机森林).
- 预测四种特定的HAC:狂妄症,吸入性肺炎,肺炎和尿路感染.
主要成果:
- 所有的ML模型在训练和测试数据集上都显示出高性能 (AUC~0.90).
- 决策树和随机森林模型显示的灵敏度高于物流回归.
- 后勤回归模型实现了最低的平均虚假阳性率;关键预测因素包括逗留时间和查尔森指数.
结论:
- 开发了HAC实时ML风险预测模型,每天计算风险得分.
- 整合ML风险检测系统可以提高患者安全和资源优化.
- 后勤回归模型在HAC风险预测方面表现出卓越的性能.
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