ArticleInternal and emergency medicine2020
Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.
Article in Internal and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Models to predict length of stay in the emergency department: a systematic literature review and appraisal.BMC emergency medicine · 2024Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Artificial Intelligence-Generated Discharge Dates and Estimation Accuracy in Hospitalized Patients.JAMA network open · 2026Article
- Shortening emergency department length of stay: Fast track, short-stay unit and acute medical unit.Exploratory research in clinical and social pharmacy · 2025Review
- Zero-shot LLM-based visual acuity extraction: a pilot study.BMC ophthalmology · 2025Article
- Predicting individual patient and hospital-level discharge using machine learning.Communications medicine · 2024Article
- Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).Internal and emergency medicine · 2024Article
- Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care.Healthcare (Basel, Switzerland) · 2024Article
- Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study.JMIR medical informatics · 2023Article
- The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review.JMIR nursing · 2023Article
- Article
- Machine-learning prediction for hospital length of stay using a French medico-administrative database.Journal of market access & health policy · 2023Article
- Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19.BMC infectious diseases · 2022Article
- Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.Internal and emergency medicine · 2022Article
- Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework.Journal of ambient intelligence and humanized computing · 2022Article
- Improving Patient Selection and Prioritization for Hospital at Home Through Predictive Modeling.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Understanding the Accuracy of Clinician Provided Estimated Discharge Dates.Journal of medical systems · 2021Observational
- Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.Internal and emergency medicine · 2021Article
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019. These data were used to assess the feasibility of predicting LOS and discharge destination using natural language processing and a variety of machine learning models. 313 patients were included in the study. The artificial neural network achieved the highest accuracy on the primary outcome of predicting whether a patient would remain in hospital for > 2 days (accuracy 0.82, area under the received operator curve 0.75, sensitivity 0.47 and specificity 0.97). When predicting LOS as an exact number of days, the artificial neural network achieved a mean absolute error of 2.9 and a mean squared error of 16.8 on the test set. For the prediction of home as a discharge destination (vs any non-home alternative), all models performed similarly with an accuracy of approximately 0.74. This study supports the feasibility of using natural language processing to predict general medical inpatient LOS and discharge destination. Further research is indicated with larger, more detailed, datasets from multiple centres to optimise and examine the accuracy that may be achieved with such predictions.
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