Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.
| Autor: | Gu S; Author Affiliations: Department of Computer Science, Center for Data Science (Ms Gu, Mr Lee, and Dr Ho), and Nell Hodgson Woodruff School of Nursing (Drs Zhang, Simpson, and Hertzberg), Emory University, Atlanta, GA., Lee EW, Zhang W, Simpson RL, Hertzberg VS, Ho JC |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Computers, informatics, nursing : CIN [Comput Inform Nurs] 2024 Mar 01; Vol. 42 (3), pp. 184-192. Date of Electronic Publication: 2024 Mar 01. |
| DOI: | 10.1097/CIN.0000000000001053 |
| Abstrakt: | 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. (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|