ArticleHealthcare informatics research2020
Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms.
Article in Healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Optimization of hospital resource scheduling efficiency based on dynamic weighted distance anomaly detection algorithm.Scientific reports · 2026Article
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- A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.BMC health services research · 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
- Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.Healthcare informatics research · 2024Article
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- Machine learning for predicting hepatitis B or C virus infection in diabetic patients.Scientific reports · 2023Article
- Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.PLoS computational biology · 2023Article
- Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence.Frontiers in psychiatry · 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
- Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19.BMC infectious diseases · 2022Article
- Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study.JMIR medical informatics · 2021Article
- Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model.Healthcare (Basel, Switzerland) · 2021Article
- Health information technology to improve care for people with multiple chronic conditions.Health services research · 2021Review
- A sequential machine learning framework for ICU and hospital length of stay prediction from admission-time data.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions.
methodsAn administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE).
resultsLSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS.
conclusionsProlonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.
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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.