Name
Title | ||
|---|---|---|
Early warning model for death of sepsis via length insensitive temporal convolutional network |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11517-022-02521-3 | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING |
Keywords | DocType | Volume |
Sepsis, Death risk, Deep learning, Temporal convolutional network (TCN), Length insensitive | Journal | 60 |
Issue | ISSN | Citations |
3 | 0140-0118 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghui Gong | 1 | 0 | 0.34 |
| Jingming Liu | 2 | 0 | 0.34 |
| Chunping Li | 3 | 0 | 1.69 |
| Wei Guo | 4 | 0 | 0.34 |
| Ruolin Wang | 5 | 0 | 0.34 |
| Zheng Chen | 6 | 0 | 0.34 |