Tsui Laboratory, Children's Hospital of Philadelphia (CHOP).
Department of Biomedical Informatics, CHOP.
Stud Health Technol Inform. 2022 Jun 6;290:660-664. doi: 10.3233/SHTI220160.
We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.
This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014-2018 at a quaternary children's hospital.
We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians.
The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956).
Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.
我们旨在从住院儿童的常规电子健康记录 (EHR) 数据中开发一种数据驱动的机器学习模型，以预测危急恶化事件。
本回顾性队列研究纳入了 2014 年至 2018 年间在一家四级儿童医院住院的内科或外科病房的所有儿科住院患者。
我们开发了一种大型数据驱动方法，并评估了三种机器学习模型来预测儿科危急恶化事件。我们使用嵌套、分层的 10 折交叉验证来评估模型。评估指标包括 C 统计量、敏感性和阳性预测值。我们还将机器学习模型与床边临床医生识别的高危观察器患者进行了比较。
该研究纳入了 34976 名独特患者的 57233 例住院入院。从 EHR 数据中确定了 3943 个变量。XGBoost 模型表现最佳（C 统计量=0.951，CI：0.946∼0.956）。
我们的数据驱动机器学习模型准确预测了患者的恶化。未来的社会技术分析将为在临床环境中的部署提供信息。
