ArticlePloS one2021
MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 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
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review.Frontiers in digital health · 2026Review
- MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2026Article
- Improving clinical decision support through interpretable machine learning and error handling in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A methodological systematic review of validation and performance of sepsis real-time prediction models.NPJ digital medicine · 2025Article
- Efficient sepsis detection using deep learning and residual convolutional networks.PeerJ. Computer science · 2025Article
- Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach.Frontiers in medicine · 2025Article
- An early sepsis prediction model utilizing machine learning and unbalanced data processing in a clinical context.Preventive medicine reports · 2024Article
- A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.Critical care (London, England) · 2024Article
- CISepsis: a causal inference framework for early sepsis detection.Frontiers in cellular and infection microbiology · 2024Article
- Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.Journal of clinical medicine · 2023Review
- Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.Scientific reports · 2023Article
- SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis.Applied clinical informatics · 2023Article
- Transferability and interpretability of the sepsis prediction models in the intensive care unit.BMC medical informatics and decision making · 2022Article
- In-sensor neural network for high energy efficiency analog-to-information conversion.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty. For instance, when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an area under the PR curve of 0.483, whereas the (less interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC and 0.359 AUPR.
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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.