脑出血
改良兰金量表
医学
接收机工作特性
基线(sea)
人工智能
人口
冲程(发动机)
集合(抽象数据类型)
机器学习
内科学
缺血性中风
计算机科学
机械工程
海洋学
环境卫生
缺血
蛛网膜下腔出血
工程类
程序设计语言
地质学
作者
Ling-Chien Hung,Yingying Su,Jui-Ming Sun,Wan‐Ting Huang,Sheng Feng Sung
标识
DOI:10.1016/j.jns.2023.120807
摘要
Intracerebral hemorrhage (ICH) is a devastating stroke type that causes high mortality rates and severe disability among survivors. Many prognostic models are available for prognosticating patients with ICH. This study aimed to investigate whether clinical narratives can improve the performance for predicting functional outcomes after ICH.This study used data from the hospital stroke registry and electronic health records. The study population (n = 1363) was randomly divided into a training set (75%, n = 1023) and a holdout test set (25%, n = 340). Five risk scores for ICH were used as baseline prognostic models. Using natural language processing (NLP), text-based markers were generated from the clinical narratives of the training set through machine learning (ML) and deep learning (DL) approaches. The primary outcome was a poor functional outcome (modified Rankin Scale score of 3 to 6) at hospital discharge. The predictive performance was compared between the baseline models and models enhanced by incorporating the text-based markers using the holdout test set.The enhanced prognostic models outperformed the baseline models, regardless of whether ML or DL approaches were used. The areas under the receiver operating characteristic curve (AUCs) of the baseline models were between 0.760 and 0.892. Adding the text-based marker to the baseline models significantly increased the model discrimination, with AUCs ranging from 0.861 to 0.914. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements.Using NLP to extract textual information from clinical narratives could improve the predictive performance of all baseline prognostic models for ICH.
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