ArticleHealthcare informatics research2013
Use of data mining techniques to determine and predict length of stay of cardiac patients.
Article in Healthcare informatics research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed.
- Predicting Hospital Length of Stay in Orthopedic Trauma Patients Using Fracture-Specific Machine Learning Models: A Multicenter Retrospective Prediction-Modeling Study.Health science reports · 2026Article
- Predicting Factors Affecting Postoperative Length of Stay in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review.Health science reports · 2026Article
- Prediction of prolonged length of stay from first 2 days of hospitalization data: a SHAP value-based variable selection method for a simplified model.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025Article
- Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.Hematology, transfusion and cell therapy · 2024Article
- Predicting hospital length of stay using machine learning on a large open health dataset.BMC health services research · 2024Article
- Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care.Healthcare (Basel, Switzerland) · 2024Article
- Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables-Bayesian Models vs. Machine Learning Models.Healthcare (Basel, Switzerland) · 2024Article
- Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning.The Journal of thoracic and cardiovascular surgery · 2023Article
- Length of Hospital Stay and Its Related Factors in Iran: A Systematic Review and Meta-Analysis.Iranian journal of public health · 2023Review
- Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods.Journal of diabetes and metabolic disorders · 2023Article
- Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables.Sensors (Basel, Switzerland) · 2023Article
- Work unit level personnel working hours and the patients' length of in-hospital stay-An administrative data approach.PLOS digital health · 2023Article
- A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making.Cancers · 2023Article
- Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm.Journal of healthcare engineering · 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
- Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi.Scientific reports · 2022Article
- Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining.Sensors (Basel, Switzerland) · 2022Article
- A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.BMC medical informatics and decision making · 2022Article
- A systematic review of the prediction of hospital length of stay: Towards a unified framework.PLOS digital health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPredicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients.
methodsData were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy.
resultsThe overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS ≤5 days, whereas 41.2% of married patients had an LOS >10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction <2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects.
conclusionsAll three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.
Indexed as
Identifiers
What 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.