ArticleBMC medical informatics and decision making2019
Recursive neural networks in hospital bed occupancy forecasting.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Forecasting hospital bed occupancy: a time series approach with prophet.BMC medical informatics and decision making · 2026Article
- Dynamic demand forecasting and capacity assessment for emergency care system: a hybrid time series and machine learning approach with evidence from Beijing.BMC health services research · 2026Article
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- Bayesian model averaging based deep learning forecasts of inpatient bed occupancy in mental health facilities.Scientific reports · 2025Article
- Review
- Time series forecasting of bed occupancy in mental health facilities in India using machine learning.Scientific reports · 2025Article
- Statistical Analysis and Forecasts of Performance Indicators in the Romanian Healthcare System.Healthcare (Basel, Switzerland) · 2025Article
- Feasibility of forecasting future critical care bed availability using bed management data.BMJ health & care informatics · 2024Article
- Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.JMIR medical informatics · 2024Article
- An intriguing vision for transatlantic collaborative health data use and artificial intelligence development.NPJ digital medicine · 2024Review
- Machine learning based forecast for the prediction of inpatient bed demand.BMC medical informatics and decision making · 2022Article
- Forecasting the Requirement for Nonelective Hospital Beds in the National Health Service of the United Kingdom: Model Development Study.JMIR medical informatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEfficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning.
methodsWe construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September).
resultsAn average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets.
conclusionsThe proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.