ArticleBMC medical informatics and decision making2022
Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.
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 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.JMIR medical informatics · 2026Article
- Prevalence and determinants of multimorbidity in older Chinese adults: a nationwide cross-sectional study using CLASS data.BMC geriatrics · 2026Article
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Disease network analysis to reveal comorbidity patterns in hospitalized patients with COPD using large-scale administrative health data.Scientific reports · 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
- Exploring the integration of medical and preventive chronic disease health management in the context of big data.Frontiers in public health · 2025Review
- Review
- Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network.Health information science and systems · 2024Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.BMC medical informatics and decision making · 2023Article
- Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately.
methodsIn this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance.
resultsThe experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions.
conclusionOur predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.
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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.