ArticleScientific reports2020
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 2025Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Predictive models for ICU patient readmission based on machine learning: A systematic review.Journal of the Intensive Care Society · 2026Article
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
- Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge.EClinicalMedicine · 2025Article
- Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.PloS one · 2025Article
- Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.Journal of clinical medicine · 2024Article
- Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.BMC medical informatics and decision making · 2024Article
- Predicting unplanned readmissions in the intensive care unit: a multimodality evaluation.Scientific reports · 2023Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Automated screening of potential organ donors using a temporal machine learning model.Scientific reports · 2023Article
- Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.International journal of environmental research and public health · 2023Article
- All-Cause Readmission or Potentially Avoidable Readmission: Which Is More Predictable Using Frailty, Comorbidities, and ADL?Innovation in aging · 2023Article
- Predictive Modeling for Readmission to Intensive Care: A Systematic Review.Critical care explorations · 2023Review
- An AI based digital-twin for prioritising pneumonia patient treatment.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2022Article
- Multimodal biomedical AI.Nature medicine · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F
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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.