ArticlePloS one2020
Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding.
Article in PloS one, 2020. 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.
- Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Leveraging nonlinear relationships and interactions to improve 30-day pneumonia readmission machine learning models.PloS one · 2026Article
- Predicting 30-Days Hospital Readmission for Patients with Heart Failure Using Electronic Health Record Embeddings: Comparative Evaluation.JMIR medical informatics · 2025Article
- Review
- Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.Bioinformatics advances · 2025Article
- Predictive modeling for identification of older adults with high utilization of health and social services.Scandinavian journal of primary health care · 2024Article
- Article
- Machine learning based readmission and mortality prediction in heart failure patients.Scientific reports · 2023Article
- Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors.ATS scholar · 2023Article
- Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions.Scientific reports · 2023Article
- Predicting 30-Day All-Cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks.IEEE journal of biomedical and health informatics · 2023Article
- Factors Associated with Early Discharge after Non-Emergent Right Colectomy for Colon Cancer: A NSQIP Analysis.Current oncology (Toronto, Ont.) · 2023Article
- Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study.Journal of medical Internet research · 2023Observational
- Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2023Article
- Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.BMC medical informatics and decision making · 2022Article
- AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.Journal of biomedical informatics · 2022Article
- Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review.International journal of cardiology. Heart & vasculature · 2021Article
- Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data.Journal of surgical oncology · 2021Article
- Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.JMIR medical informatics · 2021Article
- Explainable Tree-Based Predictions for Unplanned 30-Day Readmission of Patients With Cancer Using Clinical Embeddings.JCO clinical cancer informatics · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.
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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.