词汇
计算机科学
非结构化数据
突出
压力伤
人工智能
医学
命名实体识别
自然语言处理
医疗急救
大数据
数据挖掘
语言学
工程类
哲学
系统工程
任务(项目管理)
作者
S. Gu,Eric W. Lee,Wenhui Zhang,Roy L. Simpson,Vicki Hertzberg,Joyce C. Ho
出处
期刊:Cin-computers Informatics Nursing
日期:2023-08-23
标识
DOI:10.1097/cin.0000000000001053
摘要
Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired pressure injury is important. Prediction models use structured data more often than unstructured notes, although the latter often contain useful patient information. We hypothesize that unstructured notes, such as nursing notes, can predict hospital-acquired pressure injury. We evaluate the impact of using various natural language processing packages to identify salient patient information from unstructured text. We use named entity recognition to identify keywords, which comprise the feature space of our classifier for hospital-acquired pressure injury prediction. We compare scispaCy and Stanza, two different named entity recognition models, using unstructured notes in Medical Information Mart for Intensive Care III, a publicly available ICU data set. To assess the impact of vocabulary size reduction, we compare the use of all clinical notes with only nursing notes. Our results suggest that named entity recognition extraction using nursing notes can yield accurate models. Moreover, the extracted keywords play a significant role in the prediction of hospital-acquired pressure injury.
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