A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.

Journal: International journal of nursing studies
Published Date: May 9, 2024

Abstract

BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records.

Authors

  • Yong-Zhen Huang
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan; Department of Nursing, National Taiwan University Cancer Center, Taipei, Taiwan. Electronic address: m946111005@tmu.edu.tw.
  • Yan-Ming Chen
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan. Electronic address: m946109002@tmu.edu.tw.
  • Chih-Cheng Lin
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan. Electronic address: m946110006@tmu.edu.tw.
  • Hsiao-Yean Chiu
    School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan. Electronic address: hychiu0315@tmu.edu.tw.
  • Yung-Chun Chang
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.

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