ArticleHealth systems (Basingstoke, England)2018
Analysing repeated hospital readmissions using data mining techniques.
Article in Health systems (Basingstoke, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Acquisition of patients' EHR information under ED congestion - an empirical investigation.Health systems (Basingstoke, England) · 2025Review
- The role of Hospital-Based Cancer Registries (HBCRs) as information systems in the delivery of evidence-based integrated cancer care: a scoping review.Health systems (Basingstoke, England) · 2024Article
- Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Predicting the risk of hospital readmissions using a machine learning approach: a case study on patients undergoing skin procedures.Frontiers in artificial intelligence · 2023Article
- Comparison of Back-Propagation Neural Network, LACE Index and HOSPITAL Score in Predicting All-Cause Risk of 30-Day Readmission.Risk management and healthcare policy · 2021Article
Corrections and comments
- Erratum issued
- Republished as
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.
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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.