This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1210) Safety and Error Prevention in Health (206) Electronic Health Records (1068) Machine Learning (1501) Natural Language Processing (706) Falls Prevention in the Elderly (72) Nursing in a Hospital Setting (104) Nursing Records (14)

Published on in Vol 8, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16970, first published .
Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

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

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

Authors of this article:

Hayao Nakatani1 Author Orcid Image ;   Masatoshi Nakao1 Author Orcid Image ;   Hidefumi Uchiyama2, 3 Author Orcid Image ;   Hiroyoshi Toyoshiba3 Author Orcid Image ;   Chikayuki Ochiai1, 4 Author Orcid Image
Article Authors Cited by (38) Tweetations (2) Metrics

Journals

  1. Katsuki M, Narita N, Matsumori Y, Ishida N, Watanabe O, Cai S, Tominaga T. Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surgical Neurology International 2020;11:475 View
  2. Katsuki M, Matsuo M. Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, “I Think I Have Influenza,” Contributes to the Positivity Rate. Cureus 2021 View
  3. Ng Z, Ling L, Chew H, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of Nursing Management 2022;30(8):3654 View
  4. Tohira H, Finn J, Ball S, Brink D, Buzzacott P. Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances. Informatics for Health and Social Care 2022;47(4):403 View
  5. Ando K, Okumura T, Komachi M, Horiguchi H, Matsumoto Y, Pollard T. Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan. PLOS Digital Health 2022;1(9):e0000099 View
  6. Wang L, Zhang Y, Chignell M, Shan B, Sheehan K, Razak F, Verma A. Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study. JMIR Medical Informatics 2022;10(12):e38161 View
  7. Shimizu S, Tanabe G, Hayashi K, Churei H, Anzai T, Takahashi K, Ueno T, Fueki K. Quantitative text analysis of the mechanisms of tooth injury: Analysis of accidents in five sports that occurred in 15 years under school control. Dental Traumatology 2023;39(2):132 View
  8. Horigome T, Hino K, Toyoshiba H, Shindo N, Funaki K, Eguchi Y, Kitazawa M, Fujita T, Mimura M, Kishimoto T. Identifying neurocognitive disorder using vector representation of free conversation. Scientific Reports 2022;12(1) View
  9. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  10. Kawazoe Y, Shimamoto K, Shibata D, Shinohara E, Kawaguchi H, Yamamoto T. Impact of a Clinical Text–Based Fall Prediction Model on Preventing Extended Hospital Stays for Elderly Inpatients: Model Development and Performance Evaluation. JMIR Medical Informatics 2022;10(7):e37913 View
  11. Ando K, Okumura T, Komachi M, Horiguchi H, Matsumoto Y, Grosan C. Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?. PLOS Digital Health 2022;1(12):e0000158 View
  12. Luan Z, Zhang Z, Gao Y, Du S, Wu N, Chen Y, Peng X. Electronic health records in nursing from 2000 to 2020: A bibliometric analysis. Frontiers in Public Health 2023;11 View
  13. López M, Fernández‐Castro M, Martín‐Gil B, Muñoz‐Moreno M, Jiménez J. Auditing completion of nursing records as an outcome indicator for identifying patients at risk of developing pressure ulcers, falling, and social vulnerability: An observational study. Journal of Nursing Management 2022;30(4):1061 View
  14. Leurs W, Lammers L, Compagner W, Groeneveld M, Korsten E, van der Linden C. Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study. Aging and Health Research 2022;2(2):100078 View
  15. Hwang G, Tang K, Tu Y. How artificial intelligence (AI) supports nursing education: profiling the roles, applications, and trends of AI in nursing education research (1993–2020). Interactive Learning Environments 2024;32(1):373 View
  16. Hsu Y, Kao Y. Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis. CIN: Computers, Informatics, Nursing 2023;41(7):531 View
  17. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  18. Han W, Wang S, Gao J, Jan N. Application of Data Mining Technology-Based Nursing Risk Management in Emergency Department Care. Mathematical Problems in Engineering 2022;2022:1 View
  19. Parsons R, Cramb S, McPhail S. Clinical prediction models for hospital falls: a scoping review protocol. BMJ Open 2021;11(9):e051047 View
  20. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
  21. Ladios‐Martin M, Cabañero‐Martínez M, Fernández‐de‐Maya J, Ballesta‐López F, Belso‐Garzas A, Zamora‐Aznar F, Cabrero‐Garcia J. Development of a predictive inpatient falls risk model using machine learning. Journal of Nursing Management 2022;30(8):3777 View
  22. O'Connor S, Gasteiger N, Stanmore E, Wong D, Lee J. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. Journal of Nursing Management 2022;30(8):3787 View
  23. Parsons R, Blythe R, Cramb S, McPhail S. Inpatient Fall Prediction Models: A Scoping Review. Gerontology 2023;69(1):14 View
  24. Trinh V, Zhang S, Kovoor J, Gupta A, Chan W, Gilbert T, Bacchi S. The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review. International Journal for Quality in Health Care 2023;35(4) View
  25. Choi J, Choi E, Park D. In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study. BMC Medical Informatics and Decision Making 2023;23(1) View
  26. Cho I, Cho J, Hong J, Choe W, Shin H. Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study. Journal of the American Medical Informatics Association 2023;30(11):1826 View
  27. King C, Shambe A, Abraham J. Potential uses of AI for perioperative nursing handoffs: a qualitative study. JAMIA Open 2023;6(1) View
  28. Cho I, Kim M, Song M, Dykes P. Evaluation of an approach to clinical decision support for preventing inpatient falls: a pragmatic trial. JAMIA Open 2023;6(2) View
  29. Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, Ichikawa M, Takeshita Y, Kato T, Okumura M, Tobita M, Jakovljevic M. Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records. PLOS ONE 2024;19(1):e0296760 View
  30. Cheligeer C, Wu G, Lee S, Pan J, Southern D, Martin E, Sapiro N, Eastwood C, Quan H, Xu Y. BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study. JMIR Medical Informatics 2024;12:e48995 View
  31. Dormosh N, Abu-Hanna A, Calixto I, Schut M, Heymans M, van der Velde N. Topic evolution before fall incidents in new fallers through natural language processing of general practitioners’ clinical notes. Age and Ageing 2024;53(2) View
  32. Lukkahatai N, Han G. Perspectives on Artificial Intelligence in Nursing in Asia. Asian/Pacific Island Nursing Journal 2024;8:e55321 View
  33. Mun M, Kim A, Woo K. Natural Language Processing Application in Nursing Research. CIN: Computers, Informatics, Nursing 2024;42(12):889 View
  34. Kim A, Jeon E, Lee H, Heo H, Woo K. Risk factors for prediabetes in community‐dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights. Research in Nursing & Health 2024;47(6):620 View
  35. Lillelund C, Harbo M, Pedersen C. Prognosis of fall risk in home care clients: A noninvasive approach using survival analysis. Journal of Public Health 2024 View
  36. Huang T, Xu H, Wang H, Huang H, Xu Y, Li B, Hong S, Feng G, Kui S, Liu G, Jiang D, Li Z, Li Y, Ma C, Su C, Wang W, Li R, Lai P, Qiao J. Artificial intelligence for medicine: Progress, challenges, and perspectives. The Innovation Medicine 2023;1(2):100030 View

Books/Policy Documents

  1. Guellil I, Andres S, Guthrie B, Anand A, Zhang H, Hasan A, Wu H, Alex B. Natural Language Processing and Information Systems. View