Background:
Sepsis, a life-threatening illness caused by the body's response to an infection, is the leading cause of death worldwide and has become a global epidemiological burden. Early prediction of sepsis increases the likelihood of survival for septic patients.
Methods
The 2019 DII National Data Science Challenge enabled participating teams to develop models for early prediction of sepsis onset with de-identified electronic health records of over 100,000 unique patients. Our task is to predict sepsis onset 4 hours before its diagnosis using basic administrative and demographics, time-series vital, lab, nutrition as features. An LSTM-based model with event embedding and time encoding is proposed to model time-series prediction. We utilized the attention mechanism and global max pooling techniques to enable interpretation for the proposed deep learning model.
Results
We evaluated the performance of the proposed model on 2 use cases of sepsis onset prediction which achieved AUC scores of 0.940 and 0.845, respectively. Our team, BuckeyeAI achieved an average AUC of 0.892 and the official rank is #2 out of 30 participants.
Conclusions
Our model outperformed collapsed models (i.e., logistic regression, random forest, and LightGBM). The proposed LSTM-based model handles irregular time intervals by incorporating time encoding and is interpretable thanks to the attention mechanism and global max pooling techniques.
An interpretable deep-learning model for early prediction of sepsis in the emergency department
