# MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis

arXiv: https://arxiv.org/abs/1909.12637 · HTML: https://mlsanity.com/paper/1909.12637/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records](https://mlsanity.com/paper/2407.00840/) (2024)
- [Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records](https://mlsanity.com/paper/2308.10781/) (2023)
- [Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes](https://mlsanity.com/paper/2311.08149/) (2023)
- [SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning](https://mlsanity.com/paper/2408.08316/) (2024)
- [Improving Early Sepsis Onset Prediction Through Federated Learning](https://mlsanity.com/paper/2509.20885/) (2025)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)
- [Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets](https://mlsanity.com/paper/2512.05416/) (2025)
- [Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics](https://mlsanity.com/paper/2604.20924/) (2026)

## Abstract (arXiv)

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.