ArticleScientific reports2019
Neural networks versus Logistic regression for 30 days all-cause readmission prediction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 81 citations in OpenAlex.
- Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study.BMC medical research methodology · 2026Article
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Performance of Machine Learning Models in Predicting 30-Day General Medicine Readmissions Compared to Traditional Approaches in Australian Hospital Setting.Healthcare (Basel, Switzerland) · 2025Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- Neural network models for predicting readmission among patients undergoing peripheral vascular intervention using electronic health record data and clinical registry data.BMJ surgery, interventions, & health technologies · 2025Article
- Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study.JMIR cardio · 2024Article
- Prediction Models Using Decision Tree and Logistic Regression Method for Predicting Hospital Revisits in Peritoneal Dialysis Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Machine learning based readmission and mortality prediction in heart failure patients.Scientific reports · 2023Article
- The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study.Journal of medical Internet research · 2023Article
- Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis.Exploratory research in clinical and social pharmacy · 2023Article
- Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study.JMIR formative research · 2023Article
- [Advances in heart failure clinical research based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Predicting 30-Day All-Cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks.IEEE journal of biomedical and health informatics · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
- Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping reviewEuropean heart journal. Digital health · 2022Article
- Decision Support Systems in HF based on Deep Learning Technologies.Current heart failure reports · 2022Review
- Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure.Heart failure clinics · 2022Review
- Machine learning methods to predict presence of residual cancer following hysterectomy.Scientific reports · 2022Article
- Analyzing Patient Trajectories With Artificial Intelligence.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95% CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.
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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.