ArticleComputational intelligence and neuroscience2022
Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Development and validation of a LightGBM-based model with risk stratification for predicting early recurrence after catheter ablation for atrial fibrillation.BMC cardiovascular disorders · 2026Article
- Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Observational
- Development of an emergency department length-of-stay prediction model based on machine learning.World journal of emergency medicine · 2025Article
- Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning.Frontiers in digital health · 2025Article
- Article
- Light gradient boosting-based prediction of quality of life among oral cancer-treated patients.BMC oral health · 2024Article
- OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the massive medical needs of patients. In this process, the hospital length of stay (LOS) of different patients is a crucial reference to the management department. Therefore, building a model to predict LOS is of great significance. Five machine learning (ML) algorithms named Lasso regression (LR), ridge regression (RR), random forest regression (RFR), light gradient boosting machine (LightGBM), and extreme gradient boosting regression (XGBR) and six feature encoding methods named label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method are used to construct the regression prediction model. The Scikit-Learn toolbox on the Python platform builds the prediction model. The input is the dataset named Hospital Inpatient Discharges (SPARCS De-Identified) 2017 with 2343569 instances provided by the New York State Department of Health verify the model after removing 2.2% of the missing data, and the model ultimately uses mean squared error (MSE) and coefficient of determination (
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Registered trials
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.