ArticleScientific reports2021
Thirty-day hospital readmission prediction model based on common data model with weather and air quality data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.Scientific reports · 2025Article
- Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
- Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.Journal of clinical medicine · 2024Article
- Associations between source-apportioned PMEnvironmental research · 2023Article
- Comparison of 30-day planned and unplanned readmissions in a tertiary teaching hospital in China.BMC health services research · 2023Article
- Predictive modeling for COVID-19 readmission risk using machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.Informatics in medicine unlocked · 2022Article
- Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.Digital healthArticle
- Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations.Digital healthArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.
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