ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Prediction of 30-Day All-Cause Hospital Readmissions Using Limited Structured Electronic Health Record Data: Retrospective Comparative Study.JMIR formative research · 2026Article
- Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Integrating multiple data sources to predict all-cause readmission or mortality in patients with substance misuse.PLOS digital health · 2025Article
- Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes.Artificial intelligence in medicine · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. Models used EHR data defined by a Common Data Model. The LSTM model Area Under the Receiver Operating Characteristic Curve (AUROC) was significantly greater than that of the next best traditional model [LSTM 0.79 vs Random Forest (RF) 0.72, p<0.0001]. Experiments showed that performance of the LSTM models increased as prior encounter number increased up to 30 encounters. An LSTM model with 16 selected laboratory tests yielded equivalent performance to a model with all 981 laboratory tests. This new DL model may provide the basis for a more useful readmission risk prediction tool for diabetes patients.
Indexed as
Identifiers
37128461PMC10148287W4367669633What OpenQuestion holds
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.