ArticleScientific reports2022
Prediction algorithm for ICU mortality and length of stay using machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.International journal of emergency medicine · 2026Article
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model.Intensive care medicine experimental · 2026Article
- Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.Scientific reports · 2026Article
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- Article
- Towards Accurate and Reliable ICU Outcome Prediction: A Multimodal Learning Framework Based on Belief Function Theory using Structured EHRs and Free-Text Notes.Journal of healthcare informatics research · 2026Article
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning in critical care: Improving prediction of mortality and intensive care unit stay after cardiac surgery.JTCVS open · 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
- Association of endothelial activation and stress index with all-cause and cardiovascular mortality in patients with diabetic kidney disease: a population-based prospective study.Diabetology & metabolic syndrome · 2026Article
- Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.Journal of medical systems · 2026Article
- Development and implementation of explainable AI-based machine learning models for predicting hospital stay and treatment costs in cardiovascular patients.Scientific reports · 2025Article
- Classification of Clinical Outcomes in Hospitalized Asian Elephants Using Machine Learning and Survival Analysis: A Retrospective Study (2019-2024).Veterinary sciences · 2025Article
- Review
- AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.Science advances · 2025Observational
- Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.BMC medical informatics and decision making · 2025Article
- Combining Predictive Models of Mortality and Time-to-Discharge for Improved Outcome Assessment in Intensive Care Units.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.
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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.