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A primary goal of computational phenotype research is to conduct medicaldiagnosis. In hospital, physicians rely on massive clinical data to makediagnosis decisions, among which laboratory tests are one of the most importantresources. However, the longitudinal and incomplete nature of laboratory testdata casts a significant challenge on its interpretation and usage, which mayresult in harmful decisions by both human physicians and automatic diagnosissystems. In this work, we take advantage of deep generative models to deal withthe complex laboratory tests. Specifically, we propose an end-to-endarchitecture that involves a deep generative variational recurrent neuralnetworks (VRNN) to learn robust and generalizable features, and adiscriminative neural network (NN) model to learn diagnosis decision making,and the two models are trained jointly. Our experiments are conducted on adataset involving 46,252 patients, and the 50 most frequent tests are used topredict the 50 most common diagnoses. The results show that our model, VRNN+NN,significantly (p<0.001) outperforms other baseline models. Moreover, wedemonstrate that the representations learned by the joint training are moreinformative than those learned by pure generative models. Finally, we find thatour model offers a surprisingly good imputation for missing values.

Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning