%0杂志文章%@ 2291- 9694% I JMIR出版物%V 9%卡塔尔世界杯8强波胆分析 N 5% P e21347% T利用患者生命体征预测重症监护病房住院时间和死亡率:机器学习模型开发与验证%A Alghatani,Khalid %A Ammar,Nariman %A Rezgui,Abdelmounaam %A shabana - nejad,Arash %+新墨西哥矿业技术学院计算机科学与工程系，801 Leroy Pl, Socorro, NM, 87801，美国，1 5057204644，khalid.alghatani@student.nmt.edu %K重症监护病房(ICU) %K ICU患者监测%K机器学习%K预测模型%K生命体征测量%K临床智能%D 2021 %7 5.5.2021 %9原始论文%J JMIR Med Inform %G英文%X背景:患者监测在护理的各个阶段都至关重要。特别是，重症监护病房(ICU)患者监测有可能减少并发症和发病率，并通过使医院能够提供更高质量、更具成本效益的患者护理来提高护理质量，并提高ICU的医疗服务质量。目的:我们在此报告ICU住院时间和死亡率预测模型的开发和验证。这些模型将用于智能远程患者监测(IRPM)框架的智能ICU患者监测模块，该模块监测患者的健康状况，并在预测到不利的医疗状况时及时生成警报、操作指导或报告。方法:我们利用公开的重症监护医疗信息集市(MIMIC)数据库提取成人患者的ICU住院数据，建立两种预测模型:一种是死亡率预测模型，另一种是ICU住院时间预测模型。对于死亡率模型，我们应用了六种常用的机器学习(ML)二元分类算法来预测放电状态(存活与否)。对于住院时间模型，我们使用相同的6个ML算法进行二元分类，使用中位ICU患者群体的住院时间为2.64天。对于基于回归的分类，我们使用两种ML算法来预测天数。 We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models. %M 33949961 %R 10.2196/21347 %U https://medinform.www.mybigtv.com/2021/5/e21347 %U https://doi.org/10.2196/21347 %U http://www.ncbi.nlm.nih.gov/pubmed/33949961