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.

中文翻译：

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使用神经网络模型对纵向临床事件进行分类的半监督联合学习

深度学习神经网络模型的成功通常取决于大量标记训练数据的可访问性。但是，在许多医疗保健机构中，只有少量正确标记的数据可用，而大量未标记的数据却可用。此外，诸如医疗事件中的临床事件之类​​的输入变量通常具有纵向性质，这带来了额外的挑战。在本文中，我们提出了一种用于纵向临床事件分类的半监督联合学习方法。具体而言，我们的模型由序列生成模型和标记预测模型组成，并且使用标记和未标记的数据以联合方式端到端学习这两个部分，以获得更好的预测性能。