ArticleBMC medical informatics and decision making2022
Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Artificial Intelligence for Predicting Length of Stay for General Surgery Patients: A Systematic Review.World journal of surgery · 2026Pooled it
- Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.PloS one · 2024Pooled it
- Article
- Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.Nature communications · 2026Article
- Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.Journal of medical systems · 2025Article
- Observational
- Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.PLoS neglected tropical diseases · 2024Article
- A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.Journal of biomedical informatics · 2024Article
- Article
- A sequential machine learning framework for ICU and hospital length of stay prediction from admission-time data.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay.
methodsWe used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach.
resultsOur results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay. DISCUSSION: The results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes.
conclusionsTwo-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.
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Registered trials
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