预防中的精度:通过基于人工智能的手术场所并发症风险和成本建模来定制一次性负压伤口治疗利用率
Barrett J Larson1,2, Ashley Roakes1, Steve Yurick1
1Smith + Nephew, Inc., Pittsburgh, Pennsylvania, USA.
Surgical infections
|May 2, 2024
概括
机器学习模型可以预测手术部位并发症 (SSC) 风险,指导一次性负压伤口治疗 (sNPWT) 的战略使用. 这通过个性化治疗分配来优化患者护理和财务结果.
科学领域:
- 医疗信息学 医疗信息学
- 手术结果研究研究.
- 卫生经济学 卫生经济学
背景情况:
- 手术部位并发症 (SSC) 是常见的,可预防的医院获得的疾病.
- 一次性消极压伤疗法 (sNPWT) 有效地降低了SSC的发生率.
- 优化sNPWT分配对于基于价值的护理至关重要,平衡临床和财务结果.
研究的目的:
- 开发和验证机器学习模型,用于预测手术前的SSC风险.
- 确定各种外科专业的SSC的关键预测因素.
- 基于个性化风险评估,模拟sNPWT的成本效益.
主要方法:
- 对于10个开放程序,对Premier医疗保健数据库 (2017-2021) 的回顾性分析.
- 使用培训和验证数据集开发机器学习模型.
- 功能重要性评估以确定SSC预测因素和成本效益模拟.
主要成果:
- 机器学习模型实现了良好的预测性能 (平均AUC为76%).
- 关键预测因素包括患者年龄,肥胖和手术的紧急性.
- 模拟显示,在不同程序类型中,sNPWT的成本效益有显著的变化.
结论:
- 机器学习准确地预测患者的SSC风险,从而实现有针对性的sNPWT利用.
- 数据驱动的个性化方法优化了临床疗效和财务效率.
- 基于风险评估的sNPWT战略分配增强了基于价值的护理.
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