医学
冲程(发动机)
急诊科
接收机工作特性
急诊医学
电子健康档案
队列
健康档案
前瞻性队列研究
急性中风
医疗急救
预测建模
事件(粒子物理)
机器学习
内科学
医疗保健
计算机科学
机械工程
工程类
物理
量子力学
精神科
经济
经济增长
作者
Vida Abedi,Debdipto Misra,Durgesh Chaudhary,Venkatesh Avula,Clemens M. Schirmer,Jiang Li,Ramin Zand
标识
DOI:10.1177/17562864241239108
摘要
Stroke misdiagnosis, associated with poor outcomes, is estimated to occur in 9% of all stroke patients.We hypothesized that machine learning (ML) could assist in the diagnosis of ischemic stroke in emergency departments (EDs).The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. We performed model development and prospective temporal validation, using data from pre- and post-COVID periods; we also performed a case study on a small cohort of previously misdiagnosed stroke patients.We used structured and unstructured electronic health records (EHRs) of 56,452 patient encounters from 13 hospitals in Pennsylvania, from September 2003 to January 2021. ML pipelines, including natural language processing, were created using pre-event clinical data and provider notes in the EDs.Using pre-event information, our model's area under the receiver operating characteristics curve (AUROC) ranged from 0.88 to 0.92 with a similar range accuracy (0.87-0.90). Using provider notes, we identified five models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model AUROC ranged from 0.93 to 0.99. Model sensitivity and specificity reached 0.90 and 0.99, respectively. Four of the top five performing models were based on the post-COVID provider notes; however, no performance difference between models tested on pre- and post-COVID was observed.This study leveraged pre-event and at-encounter level EHR for stroke prediction. The results indicate that available clinical information can be used for building EHR-based stroke prediction models and ED stroke alert systems.
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