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Cited by 63Machine Learning in HealthcareBalance, Gait, and Falls PreventionNursing Diagnosis and Documentation

Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

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Nakatani, Hayao, Nakao, Masatoshi, Uchiyama, Hidefumi, Toyoshiba, Hiroyoshi, Ochiai, Chikayuki·2020·JMIR Medical Informatics
DOI 10.2196/16970PMC PMC7203618

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Abstract

Background: Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective: The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods: The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results: The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions: We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

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  • AI using EHR data has become much more general and accurate in the last few years, allowing prediction of perioperative events and learning effective treatment strategies.

    “AI using EHR data has become much more general and accurate in the last few years,35,36 allowing prediction of perioperative events37–41 and learning effective treatment strategies.42”

    From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [41]

    The Discussion (Principal Results; page unavailable) says recent machine-learning developments have broadened applications, and the Results report accurate prediction of inpatient falls from nursing records. The paper does not establish that EHR-based AI has become more accurate generally over the last few years. The perioperative-event and treatment-strategy claims carry other citations, so they are not attributed to this source. (LLM, conf=0.83)

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Research works printed but not linked: 2

  1. [23]Mikolov T Chen K Corrado G arXiv.org 2013 2020-04-08 Efficient estimation of word representations in vector space https://arxiv.org/abs/1301.3781 · arXiv
  2. [25]Dai A Olah C Le Q arXiv.org 2015 2020-04-08 Document embedding with paragraph vectors https://arxiv.org/abs/1507.07998 · arXiv

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Authors
Nakatani, H., Nakao, M., Uchiyama, H., Toyoshiba, H., & Ochiai, C.
Affiliations
NTT Medical Center (JP)
Corresponding author
Uchiyama, Hidefumi
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2020-04-22
Volume
8
Issue
4
Pages
e16970
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/16970
PMC
PMC7203618
OpenAlex
W3000817905
Cited by
62
References
34
Citations by year
2026: 6 · 2025: 9 · 2024: 11 · 2023: 10 · 2022: 18 · 2021: 7 · 2020: 1
FWCI
3.64
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
  • Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
Keywords
inpatient falls, fall risk prediction, nursing records, natural language processing, electronic health records, fall risk factors, psychotropic drugs, mobility (physical function), inpatient safety

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