Faster Healthcare Time Series Classification for Boosting Mortality Early Warning System

Yanke Hu, Raj Subramanian, Wangpeng An, Na Zhao, Weili Wu

Abstract: Electronic Health Record (EHR) and healthcare claim data provide rich clinical information for time series analysis. In this work, we provide a different angle of solving healthcare multivariate time series classification by turning it into a computer vision problem. We propose a Convolutional Feature Engineering (CFE) methodology, that can effectively extract long sequence dependency time series features. Combined with LightGBM, it can achieve the state-of-the-art results with 35X speed acceleration compared with LSTM based approaches on MIMIC-III In Hospital Mortality benchmark task. We deploy CFE based LightGBM into our Mortality Early Warning System at Humana, and train it on 1 million member samples. The offline metrics shows that this new approach generates better-quality predictions than previous LSTM based approach, and meanwhile greatly decrease the training and inference time.