2017/11/12 by Shiyue Zhang, Pengtao Xie, Zhang, Shiyue +5
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Time Series Analysis and Forecasting #Traditional Chinese Medicine Studies
paper · pdf · doi:10.48550/arxiv.1711.04329
openalex publication_date 2017/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A primary goal of computational phenotype research is to conduct medical diagnosis. In hospital, physicians rely on massive clinical data to make diagnosis decisions, among which laboratory tests are one of the most important resources. However, the longitudinal and incomplete nature of laboratory test data casts a significant challenge on its interpretation and usage, which may result in harmful decisions by both human physicians and automatic diagnosis systems. In this work, we take advantage of deep generative models to deal with the complex laboratory tests. Specifically, we propose an end-to-end architecture that involves a deep generative variational recurrent neural networks (VRNN) to learn robust and generalizable features, and a discriminative neural network (NN) model to learn diagnosis decision making, and the two models are trained jointly. Our experiments are conducted on a dataset involving 46,252 patients, and the 50 most frequent tests are used to predict the 50 most common diagnoses. The results show that our model, VRNN+NN, significantly (p<0.001) outperforms other baseline models. Moreover, we demonstrate that the representations learned by the joint training are more informative than those learned by pure generative models. Finally, we find that our model offers a surprisingly good imputation for missing values.