ArticleBMC health services research2022
Effective hospital readmission prediction models using machine-learned features.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.Journal of child and adolescent psychopharmacology · 2026Article
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- The SUPREME Index for 30-day all-cause readmission: development and internal validation of a machine learning-based risk stratification index in community-dwelling adults.BMC medical informatics and decision making · 2026Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- A Blueprint for Partnership between AI and MD.Delaware journal of public health · 2026Article
- Federated learning for heterogeneous electronic health record systems with cost effective participant selection.Scientific reports · 2026Article
- Evaluation of the Performance of an Artificial Intelligence-Based Classification Model for Pediatric Maxillofacial Morphology.Diagnostics (Basel, Switzerland) · 2025Article
- Effect of disability, homelessness, and neighborhood marginalization on risk adjustment for hospital performance measurement.American journal of epidemiology · 2025Article
- Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.Bioengineering (Basel, Switzerland) · 2025Article
- Advancements in Managing Choledocholithiasis and Acute Cholangitis in the Elderly: A Comprehensive Review.Cureus · 2025Review
- Review
- Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.PloS one · 2025Article
- A Qualitative Study Examining the Unintended Consequences from Implementing a Case Management Team to Reduce Avoidable Hospital Readmission in Singapore.Health services insights · 2025Article
- Leveraging machine learning in nursing: innovations, challenges, and ethical insights.Frontiers in digital health · 2025Review
- Support Vector Machines: A Literature Review on Their Application in Analyzing Mass Data for Public Health.Cureus · 2025Review
- Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API.Frontiers in digital health · 2025Review
- Survival models and longitudinal medical events for hospital readmission forecasting.BMC health services research · 2024Article
- Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital readmissions are one of the costliest challenges facing healthcare systems, but conventional models fail to predict readmissions well. Many existing models use exclusively manually-engineered features, which are labor intensive and dataset-specific. Our objective was to develop and evaluate models to predict hospital readmissions using derived features that are automatically generated from longitudinal data using machine learning techniques.
methodsWe studied patients discharged from acute care facilities in 2015 and 2016 in Alberta, Canada, excluding those who were hospitalized to give birth or for a psychiatric condition. We used population-level linked administrative hospital data from 2011 to 2017 to train prediction models using both manually derived features and features generated automatically from observational data. The target value of interest was 30-day all-cause hospital readmissions, with the success of prediction measured using the area under the curve (AUC) statistic.
resultsData from 428,669 patients (62% female, 38% male, 27% 65 years or older) were used for training and evaluating models: 24,974 (5.83%) were readmitted within 30 days of discharge for any reason. Patients were more likely to be readmitted if they utilized hospital care more, had more physician office visits, had more prescriptions, had a chronic condition, or were 65 years old or older. The LACE readmission prediction model had an AUC of 0.66 ± 0.0064 while the machine learning model's test set AUC was 0.83 ± 0.0045, based on learning a gradient boosting machine on a combination of machine-learned and manually-derived features.
conclusionApplying a machine learning model to the computer-generated and manual features improved prediction accuracy over the LACE model and a model that used only manually-derived features. Our model can be used to identify high-risk patients, for whom targeted interventions may potentially prevent readmissions.
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