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Design and Analysis for Fall Detection System Simplification
08:05

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Effects of Language Differences on Inpatient Fall Detection Using Deep Learning.

Insook Cho1, EunJu Lee1, Dong-Geon Lee2

  • 1Nursing Department, Inha University, Incheon, Republic of Korea.

Studies in Health Technology and Informatics
|March 1, 2024
PubMed
Summary
This summary is machine-generated.

This study shows language differences impact natural language processing (NLP) for identifying patient falls in nursing notes. NLP models performed differently across Korean and English text, affecting fall detection accuracy.

Keywords:
Inpatient fallsdeep learningevent detectionnursing notestext data

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Area of Science:

  • Clinical Informatics
  • Natural Language Processing
  • Healthcare Data Analysis

Background:

  • Unstructured nursing notes contain valuable clinical information.
  • Accurate identification of patient falls is crucial for patient safety and quality improvement.
  • Natural language processing (NLP) offers potential for extracting clinical events from text.

Purpose of the Study:

  • To investigate the influence of linguistic variations between Korean and English on NLP performance.
  • To evaluate the effectiveness of NLP in classifying inpatient falls from nursing notes in different languages.
  • To identify challenges and opportunities for cross-lingual NLP in healthcare.

Main Methods:

  • Utilized a dataset of unstructured nursing notes in Korean and English.
  • Applied NLP techniques for text classification to identify instances of inpatient falls.
  • Compared the performance metrics of NLP models across the two languages.

Main Results:

  • Significant differences in NLP performance were observed between Korean and English notes.
  • Linguistic features unique to each language presented distinct challenges for fall identification.
  • Model accuracy varied, indicating language-specific optimization may be required.

Conclusions:

  • Language differences demonstrably affect the performance of NLP in clinical text analysis.
  • Cross-lingual NLP for healthcare requires careful consideration of linguistic nuances.
  • Further research is needed to develop robust, language-agnostic NLP solutions for patient safety events.