%0期刊文章%@ 1438- 8871% I JMIR出版物%V 24卡塔尔世界杯8强波胆分析% N 6% P e34295% T基于机器学习的不同医院不同临床风险预测模型:现场表演评价%A孙，Hong %A德普拉特雷，Kristof %A Meesseman,Laurent %A Cabanillas Silva,Patricia %A Szymanowsky,Ralph %A Fliegenschmidt,Janis %A Hulde,Nikolai %A von Dossow,Vera %A Vanbiervliet,Martijn %A De baerdemaker,Jos %A Roccaro-Waldmeyer,Diana M %A Stieg,Jörg %A Domínguez Hidalgo,Manuel %A Dahlweid,Fried-Michael %+ Dedalus Healthcare, Roderveldlaan 2，安特卫普，2600，比利时，32 3444 8108，hong.sun@dedalus.com %K机器学习%K临床风险预测%K预测%K模型%K模型评估%K可扩展性%K风险%K现场临床工作流程%K谵妄%K脓毒症%K急性肾损伤%K肾%K EHR %K电子健康记录%K工作流程%K算法%D 2022 %7 7.6.2022 %9原始论文%J J医学互联网Res %G英文%X背景:机器学习算法目前被广泛应用于临床领域，以生成可以预测临床风险事件的模型。大多数模型是用回顾性数据开发和评估的，很少有模型是在临床工作流程中评估的，更少的是报告不同医院的表现。在这项研究中，我们为三家不同医院的三种不同用例提供了临床风险预测模型在现场临床工作流程中的详细评估。目的:本研究的主要目的是评估真实临床工作流程中的临床风险预测模型，并将其在这些设置中的表现与使用回顾性数据时的表现进行比较。我们还通过将我们的调查应用于三家不同医院的三个不同用例来概括结果。方法:我们利用回顾性数据，针对三家不同医院的三种用例(即谵妄、败血症和急性肾损伤)训练临床风险预测模型。我们使用机器学习，特别是深度学习来训练基于Transformer模型的模型。 The models were trained using a calibration tool that is common for all hospitals and use cases. The models had a common design but were calibrated using each hospital’s specific data. The models were deployed in these three hospitals and used in daily clinical practice. The predictions made by these models were logged and correlated with the diagnosis at discharge. We compared their performance with evaluations on retrospective data and conducted cross-hospital evaluations. Results: The performance of the prediction models with data from live clinical workflows was similar to the performance with retrospective data. The average value of the area under the receiver operating characteristic curve (AUROC) decreased slightly by 0.6 percentage points (from 94.8% to 94.2% at discharge). The cross-hospital evaluations exhibited severely reduced performance: the average AUROC decreased by 8 percentage points (from 94.2% to 86.3% at discharge), which indicates the importance of model calibration with data from the deployment hospital. Conclusions: Calibrating the prediction model with data from different deployment hospitals led to good performance in live settings. The performance degradation in the cross-hospital evaluation identified limitations in developing a generic model for different hospitals. Designing a generic process for model development to generate specialized prediction models for each hospital guarantees model performance in different hospitals. %M 35502887 %R 10.2196/34295 %U //www.mybigtv.com/2022/6/e34295 %U https://doi.org/10.2196/34295 %U http://www.ncbi.nlm.nih.gov/pubmed/35502887