Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
This work addresses the critical problem of early sepsis prediction for healthcare providers, but it is incremental as it builds on existing LSTM methods with a preprocessing step.
The paper tackled early sepsis prediction from electronic health records by proposing a model that aggregates heterogeneous clinical events before applying LSTM to capture temporal interactions, achieving a utility score of 0.321 on the PhysioNet/Computing in Cardiology Challenge 2019 test set.
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventive treatment. However, the early prediction is challenging because patients' sequential data in EHR contains temporal interactions of multiple clinical events. And capturing temporal interactions in the long event sequence is hard for traditional LSTM. Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM. Our proposed Heterogeneous Event Aggregation can not only shorten the length of clinical event sequence but also help to retain temporal interactions of both categorical and numerical features of clinical events in the multiple heads of the aggregation representations. In the PhysioNet/Computing in Cardiology Challenge 2019, with the team named PKU_DLIB, our proposed model, in high efficiency, achieved utility score (0.321) in the full test set.