ArticlePloS one2019
An attention based deep learning model of clinical events in the intensive care unit.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.Journal of healthcare engineering · 2023Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- PathSepsisNet: a pathway-aware temporal graph attention network integrating inflammatory crosstalk dynamics for early prediction of multi-organ dysfunction in sepsis.Purinergic signalling · 2026Article
- MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating phenotypic information of obstructive sleep apnea and deep representation of sleep-event sequences for cardiovascular risk prediction.BMC medical informatics and decision making · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model.BMC medical informatics and decision making · 2025Article
- The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care.Current cardiology reports · 2025Review
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Machine Learning Models for the Early Real-Time Prediction of Deterioration in Intensive Care Units-A Novel Approach to the Early Identification of High-Risk Patients.Journal of clinical medicine · 2025Article
- Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.BMC public health · 2025Article
- A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition.Scientific reports · 2025Article
- Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.PloS one · 2025Article
- Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Machine learning for predicting acute myocardial infarction in patients with sepsis.Scientific reports · 2024Article
- Assessment of the relationship between central venous pressure waveform and the severity of tricuspid valve regurgitation using data science.Scientific reports · 2024Article
- Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.Journal of clinical medicine · 2024Article
- Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques.Bone reports · 2024Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in the MIMIC-III dataset. These models achieved next-day predictive AUC of 0.876 for sepsis, 0.823 for MI, and 0.833 for vancomycin administration. Attention maps built from these models highlighted those times when input variables most influenced predictions and could provide a degree of interpretability to clinicians. These models appeared to attend to variables that were proxies for clinician decision-making, demonstrating a challenge of using flexible deep learning approaches trained with EHR data to build clinical decision support. While continued development and refinement is needed, we believe that such models could one day prove useful in reducing information overload for ICU physicians by providing needed clinical decision support for a variety of clinically important tasks.
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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.