Sepsis Prediction with Temporal Convolutional Networks
This work addresses sepsis prediction for ICU patients, but it is incremental as it applies an existing method to a specific dataset.
The authors tackled sepsis onset prediction using a temporal convolutional network trained on MIMIC III ICU data, achieving superior performance compared to other machine learning models in binary classification.
We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall under the definition of sepsis at the time of admission. Benchmarked with several machine learning models, our model is superior on this binary classification task, demonstrates the prediction power of convolutional networks for temporal patterns, also shows the significant impact of having longer look back time on sepsis prediction.