# Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

arXiv: https://arxiv.org/abs/1905.08547 · HTML: https://mlsanity.com/paper/1905.08547/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 17 of 17)

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- [Early Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records](https://mlsanity.com/paper/2509.18145/) (2025)
- [Closing Gaps: An Imputation Analysis of ICU Vital Signs](https://mlsanity.com/paper/2510.24217/) (2025)
- [When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion](https://mlsanity.com/paper/2602.23614/) (2026)

## Abstract (arXiv)

Objective: 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. Methods: 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. Results: 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, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Conclusions: Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy.