Dynamic Feature Selection via Reinforcement Learning

Abstract

Monte Carlo REINFORCE is used to design an algorithm to not only find the optimal deep learning architecture but also the optimal set of features that can maximize the performance of the said deep learning model. The algorithm is applied to the problem of predicting the onset of severe sepsis (before 4 hours) and the results are compared with existing severe sepsis literature. Sepsis is a life-threatening condition caused by the patient body’s extreme response to an infection, causing tissue damage and multiple organ failures. MIMIC-III dataset, a publicly available medical dataset is used for all the experiments. Apart from the 6 common vital sign measurements, the dataset also contains 127 physiological and laboratory features to predict the onset of severe sepsis, mostly observed in intensive care units (ICUs). Reinforcement learning is used to reduce the number of features (from 133) without sacrificing peak model performance that uses all 133 features. Among the discovered deep learning models, the CNN-LSTM model using 110 features achieves the best performance: an AUC of 0.933 in predicting the onset of severe sepsis.