ArticleMedical & biological engineering & computing2022
Early warning model for death of sepsis via length insensitive temporal convolutional network.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 3 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Efficient sepsis detection using deep learning and residual convolutional networks.PeerJ. Computer science · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a life-threatening systemic syndrome characterized by various biological, biochemical, and physiological abnormalities. Due to its high mortality, identifying sepsis patients with high risk of in-hospital death early and accurately will help doctors make optimal clinical decisions and reduce the mortality of sepsis patients. In this paper, we propose a length insensitive TCN-based model to predict sepsis patient's death risk in the future k hours, which is the first work for sepsis death risk early warning model only based on vital signs time series to our best knowledge. Furthermore, we design residual connections between temporal residual blocks to improve the prediction performance and stability especially on short input sequences. We validate and evaluate our model on two freely-available datasets, i.e., MIMIC-IV and eICU, from which 16,520 and 29,620 patients are selected respectively. The experiment results show that our model outperforms LSTM and other machine learning methods, as it has the highest sensitivity and Youden index in almost all cases. Meanwhile, the Youden index of the TCN-based model only slightly decreases by 0.0233 and 0.0307 when the time range of the input sequence changes from 24 to 4 h for k equal to 6 and 12, respectively.
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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.