ArticleJournal of biomedical informatics2018
Modeling asynchronous event sequences with RNNs.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- Application of a natural language processing algorithm to early asthma ascertainment for adults in the era of electronic health records.The journal of allergy and clinical immunology. Global · 2026Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records.Nature communications · 2024Article
- Article
- 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
- KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.The Lancet. Digital health · 2022Article
- RVAgene: generative modeling of gene expression time series data.Bioinformatics (Oxford, England) · 2021Article
- Influential Usage of Big Data and Artificial Intelligence in Healthcare.Computational and mathematical methods in medicine · 2021Review
- Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.Journal of medical Internet research · 2020Article
- Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations.Patterns (New York, N.Y.) · 2020Article
- Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.The Journal of allergy and clinical immunology · 2020Review
- Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically.Diabetes, metabolic syndrome and obesity : targets and therapy · 2020Article
- Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing.International journal of medical informatics · 2019Article
- Temporal biomedical data analytics.Journal of biomedical informatics · 2019Article
- Deep learning and alternative learning strategies for retrospective real-world clinical data.NPJ digital medicine · 2019Review
- A Novel Time-Aware Deep Learning Model Predicting Myopia in Children and Adolescents.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time. Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time. We show several approaches to explicitly incorporating relative time into a recurrent neural network (RNN) model that improve the overall classification of patients into those with no asthma, those with persistent asthma, those in long-term remission, and those who have experienced relapse. We also compare and contrast these results with those in an inpatient intensive care setting.
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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.