TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records
Open accessAbstract
Motivation: Electronic health records (EHRs) represent a comprehensive resource of a patient’s medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient’s clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit. Results: The results of the experiments conducted on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer’s Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions. Availability and implementation: https://github.com/bozdaglab/TA-RNN.
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Recurrent neural networks, including time-aware versions and attention-based methods, allow models to handle irregularly sampled EHR sequences and decide which time points are most clinically meaningful.
“Usually, recurrent neural networks (RNNs), including time-aware versions and attention-based methods, allow models to handle irregularly sampled EHR sequences and decide which time points are most clinically meaningful46.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [46]
The Introduction says time-aware RNN extensions can address irregular intervals and attention can identify significant time points. But it also says ordinary RNNs assume equal gaps between visits, so the claim is too broad in suggesting RNNs generally handle irregular sampling. (LLM, conf=0.87)
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- Authors
- Al Olaimat, M., Bozdag, S., & Initiative, F. T. A. D. N.
- Affiliations
- Department of Computer Science and Engineering, University of North Texas , Denton, TX 76203, United States
- University of North Texas (US)
- Corresponding author
- Bozdag, Serdar
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-06-28
- Volume
- 40
- Issue
- Supplement_1
- Pages
- i169-i179
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11211851
- OpenAlex
- W4391335182
- Cited by
- 30
- References
- 32
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 2
- FWCI
- 4.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- University of North Texas Digital Library (University of North Texas) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (U01 AG024904, R35GM133657)
- Department of Defense (W81XWH-12–2-0012)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- NIA (U24 AG072122, P30 AG062429)
- National Institute of General Medical Sciences
- University of North Texas
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, attention mechanism, time embedding, irregular time intervals, explainable deep learning, Alzheimer's disease prediction, mortality prediction, autoencoder
- MeSH terms
- Deep Learning, Alzheimer Disease, Humans, Neural Networks, Computer, Electronic Health Records
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