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.
BibTeX
@inproceedings{iros2020_fasterhealthcare,
title = {Faster Healthcare Time Series Classification for Boosting Mortality Early Warning System},
author = {Yanke Hu and Raj Subramanian and Wangpeng An and Na Zhao and Weili Wu},
booktitle = {IROS 2020},
year = {2020}
}