Integrating Feature Selection Approaches with Recurrent Neural Networks to Predict Early Onset Sepsis in Critical Care Patients

Jill Cates, Kevin Ha, Gabe Musso


Early detection of sepsis in hospitalized critical care patients is crucial for improving survival. As a result, there is an important need to develop modern machine learning approaches to support the expert judgement of a treating physician.

In this study, we aim to develop a robust sepsis prediction model using physiological data from the 2019 PhysioNet Challenge. In our preliminary analysis, we trained a recurrent neural network using long short-term memory (LSTM) and achieved an accuracy score of 92%. While the LSTM parameters themselves can be optimized in well-understood ways to produce a more accurate classifier, the impact of pre-processing parameters on sepsis prediction performance remain largely unknown. Thus, we looked to more systematically consider the impact of upstream decisions made in processing the data before training a model. Specifically, we built a framework that iterates over various preprocessing techniques and machine learning models, with the goal of identifying optimal parameters for sepsis prediction. We consider two feature selection methods: stepwise forward feature selection, and manual feature selection performed by a physician with domain knowledge. We then compare three classification models: 1) random forest classification using feature extraction of time series characteristics, 2) LSTM, and 3) LSTM with fully convolutional neural network (LSTM-FCN). The models were trained using a subset of 4000 patient records containing between 10 and 256 hourly observations. In the training set, 42% of patients presented with sepsis at some point during their hospitalization. Each model was validated on a separate set of 4000 patient records using a utility function that rewards early prediction of sepsis and penalizes late or missed predictions of sepsis. Together, our end-to-end framework facilitates the exploration of different preprocessing and machine learning techniques in an efficient, automated manner.