Semi-supervised joint learning for longitudinal clinical events classification using neural network models

The success of deep learning neural network models often relies on the accessibility of a large number of labelled training data. In many health care settings, however, only a small number of accurately labelled data are available while unlabelled data are abundant. Further, input variables such as clinical events in the medical settings are usually of longitudinal nature, which poses additional challenges. In this paper, we propose a semi-supervised joint learning method for classifying longitudinal clinical events. Specifically, our model consists of a sequence generative model and a label prediction model, and the two parts are learned end to end using both labelled and unlabelled data in a joint manner to obtain better prediction performance. Using five mortality-related classification tasks on the Medical Information Mart for Intensive Care (MIMIC) III database, we demonstrate that the proposed method outperforms the purely supervised method that uses labelled data only and existing two-step semi-supervised methods.