The hospital readmission prediction becomes a significant task for healthcare systems and patients. Many predictive models have been developed and make progress in this task. However, most of them roughly combine the patient’s long-term(e.g., the history of present illness) and short-term(e.g., the performed laboratory test results when the patients are discharged) information without considering the inner distinction between them. In this paper, we propose a new approach for hospital readmission prediction based on transformation from numerical features to natural language, which makes better fusion of these two kinds of information. Through a rule-based transformation, the original numerical features are transformed into corresponding descriptive short sentences based on medical knowledge. Meanwhile, with the help of public well pre-trained character embeddings, our model can incorporate the prior semantic knowledge into the data. Moreover, by using the long-term information as the query of short-term feature attention mechanism, our model can capture the effective information in the short-term features from a more global perspective, and better incorporates the long-term and short-term information. Extensive experiment results on a real dataset demonstrate the effectiveness and superiority of our proposed model compared with the baseline methods.

医院再入院预测成为医疗系统和患者的重要任务。已经开发了许多预测模型，并在此任务中取得了进展。然而，它们中的大多数粗略地结合了患者的长期（例如，当前病史）和短期（例如，患者出院时进行的实验室测试结果）信息，而没有考虑它们之间的内在区别。在本文中，我们提出了一种从数字特征到自然语言转换的医院入院率预测新方法，可以更好地融合这两种信息。通过基于规则的转换，基于医学知识，将原始数字特征转换为相应的描述性简短句子。与此同时，借助预先训练有素的公共字符嵌入，我们的模型可以将先验的语义知识整合到数据中。此外，通过使用长期信息作为短期特征关注机制的查询，我们的模型可以从更全局的角度捕获短期特征中的有效信息，并更好地融合长期和短期信息。在真实数据集上的大量实验结果表明，与基线方法相比，我们提出的模型的有效性和优越性。并更好地结合长期和短期信息。在真实数据集上的大量实验结果表明，与基线方法相比，我们提出的模型的有效性和优越性。并更好地结合长期和短期信息。在真实数据集上的大量实验结果表明，与基线方法相比，我们提出的模型的有效性和优越性。