ArticleDiagnostics (Basel, Switzerland)2021
Predicting Prolonged Length of ICU Stay through Machine Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- 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
- Improving Hospital Length of Stay Prediction through Heterogeneous Data Integration from MIMIC-III Records.Research square · 2025Article
- Design and Development of a Machine Learning Model for Predicting ICU Patients' Length of Stay.Cureus · 2025Article
- Development of an emergency department length-of-stay prediction model based on machine learning.World journal of emergency medicine · 2025Article
- Predictors of Prolonged Intensive Care Unit Stay and In-Hospital Mortality Following Cardiac Surgery: An Integrated Analysis from the PROCARD-ATI Study.Journal of clinical medicine · 2025Article
- A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.BMC health services research · 2025Article
- Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis.Journal of inflammation research · 2025Article
- Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.Journal of medical Internet research · 2024Article
- Transformer-based model for predicting length of stay in intensive care unit in sepsis patients.Frontiers in medicine · 2024Article
- Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU.Respiratory care · 2023Article
- Machine Learning for Benchmarking Critical Care Outcomes.Healthcare informatics research · 2023Article
- Predictive models for short-term mortality and length of hospital stay among adults with community-onset bacteraemia before and during the COVID-19 pandemic: application of early data dynamics.BMC infectious diseases · 2023Article
- Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis.Frontiers in artificial intelligence · 2023Article
- Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.Frontiers in public health · 2023Article
- Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study.Journal of multidisciplinary healthcare · 2023Article
- A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease.Frontiers in medicine · 2023Article
- Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.Frontiers in medicine · 2022Article
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6 authors.
Funding
Abstract
This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes.
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