ArticleJournal of the American Medical Informatics Association : JAMIA2020
Temporal convolutional networks allow early prediction of events in critical care.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Performance fairness of neural network models in early risk assessment of inpatients with varying severity: a retrospective study.BMC medical informatics and decision making · 2026Article
- Longitudinal multisource clinical model for early lung cancer risk stratification and screening.BMJ health & care informatics · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models.Frontiers in surgery · 2026Review
- Beyond static risk scores: dynamic world models simulating patient-specific trajectories to inform preoperative risk mitigation strategies.Frontiers in medicine · 2026Article
- Article
- Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data.EClinicalMedicine · 2025Article
- Comparison of time-series models for predicting physiological metrics under sedation.Journal of clinical monitoring and computing · 2025Article
- Cognition and behavior in neurofibromatosis type 1: report and perspective from the Cognition and Behavior in NF1 (CABIN) Task Force.Genes & development · 2025Article
- Article
- Clinical outcome-guided deep temporal clustering for disease progression subtyping.Journal of biomedical informatics · 2024Article
- Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture.Scientific reports · 2024Article
- Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction.Scientific reports · 2022Article
- Estimated PaoCritical care explorations · 2021Article
- Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Severity Trajectories of Pediatric Inpatients Using the Criticality Index.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2021Article
- Innovation is key for advancing the science of biomedical and health informatics and for publishing in JAMIA.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome.Frontiers in big data · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveClinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action. MATERIALS AND
methodsWe use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014-2018. For each hour of each admission, we predict events in the subsequent 1-6 hours. We compare performance with other models including a recurrent neural network.
resultsOur model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781-0.790) and 0.738 (0.732-0.743) for up- and down-titrating FiO2, 0.574 (0.519-0.625) for extubation, 0.139 (0.117-0.162) for intubation, 0.533 (0.492-0.572) for starting noradrenaline, 0.441 (0.433-0.448) for fluid challenge, and 0.315 (0.282-0.352) for death. DISCUSSION: Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions.
conclusionTemporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.
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