使用高吞吐量实时数据的手术内持续时间预测模型的实施和未来绩效评估
York Jiao1, Thomas Kannampallil1,2
1Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.
BJA open
|May 15, 2024
概括
一个新的机器学习 (ML) 算法准确地预测实时的手术持续时间,改善术后结果. 预测模型在10个月的评估期内证明了持续的性能.
科学领域:
- 麻醉学 麻醉学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 准确的实时预测手术期间的持续时间对于提高术后结果至关重要.
- 开发了一个数据管道,从麻醉记录中提取实时数据.
- 一个预测性机器学习 (ML) 算法被实施和部署.
研究的目的:
- 实施和评估一个实时的ML算法来预测手术内外科手术的持续时间.
- 评估随着时间的推移,ML模型预测的准确性和稳定性.
主要方法:
- 通过第三方平台从电子健康记录中提取临床变量.
- 使用了以前开发的ML模型,在3个月的数据上进行训练.
- 模型性能在10个月内使用连续排列概率得分 (CRPS) 进行评估.
主要成果:
- 机器学习模型在62,000个程序中做出了600多万个预测.
- 在ML模型中,平均CRPS达到27.19分钟,显著超过预定的持续时间 (51.66分钟).
- 线性回归证实在10个月的测试期内没有性能偏移.
结论:
- 一个实时的ML算法用于预测手术持续时间已经成功实施和部署.
- 展望评估证实,该模型的预测性能在10个月内保持稳定.
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