ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Improving length of stay prediction using a hidden Markov model.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Relationships Among Comorbidities, Disease Severity, and Hospitalization Duration in the United States Using the Healthcare Cost and Utilization Project (HCUP) Database.Journal of clinical medicine · 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
- Predicting hospital length of stay using machine learning on a large open health dataset.BMC health services research · 2024Article
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- Machine Learning for Benchmarking Critical Care Outcomes.Healthcare informatics research · 2023Article
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
- Article
- Understanding basic principles of Artificial Intelligence: a practical guide for intensivists.Acta bio-medica : Atenei Parmensis · 2022Article
- A comparison of statistical methods for modeling count data with an application to hospital length of stay.BMC medical research methodology · 2022Article
- Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients.Frontiers in cardiovascular medicine · 2022Article
- Hidden Markov model segmentation to demarcate trajectories of residual apnoea-hypopnoea index in CPAP-treated sleep apnoea patients to personalize follow-up and prevent treatment failure.The EPMA journal · 2021Article
- Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach.Proceedings of machine learning research · 2021Article
- Artificial Intelligence in the Intensive Care Unit.Critical care (London, England) · 2020Review
- Article
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating length of stay of intensive care unit patients is crucial to reducing health care costs. This can help physicians intervene at the right time to prevent adverse outcomes for the patients. Moreover, resource allocation can be optimized to ensure appropriate hospital staff levels. Yet the length of stay prediction is very hard, as physicians can only accurately estimate half of their patient population. As electronic health records have become more prevalent, researchers can harness the power of machine learning to accurately predict the length of stay. We propose a hidden Markov model-based framework to predict the length of stay using some of patients' physiological measurements during the first 48 hours of their admission to the intensive care unit. We show that this model can succinctly capture temporal patient representations. We demonstrate the potential of our framework on real ICU data in consistently outperforming most of the existing baselines.
Identifiers
31258996PMC6568102What OpenQuestion holds
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.