The goal of computer-aided diagnosis is to predict patient’s diseases based on patient’s clinical data. The development of deep learning technology provides new help for clinical diagnosis. In this paper, we propose a new sequence generation model for multi-disease diagnosis prediction based on numerical feature transformation. Our model simultaneously uses patient’s laboratory test results and clinical text as input to diagnose and predict the disease that the patient may have. According to medical knowledge, our model can transform numerical features into descriptive text features, thereby enriching the semantic information of clinical texts. Besides, our model uses attention-based sequence generation methods to achieve the diagnosis of multiple diseases and better utilizes the correlation information between multiple diseases. We evaluate our model’s performance on a dataset of respiratory diseases from the real world, and experimental results show that our model’s accuracy reaches 42.75%, and the F1 score reaches 65.65%, which is better than many other methods. It is suitable for the accurate diagnosis of multiple diseases.

中文翻译：

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基于数值特征变换的多病诊断序列生成模型

计算机辅助诊断的目标是根据患者的临床数据预测患者的疾病。深度学习技术的发展为临床诊断提供了新的帮助。在本文中，我们提出了一种新的基于数值特征变换的多疾病诊断预测序列生成模型。我们的模型同时使用患者的实验室测试结果和临床文本作为输入来诊断和预测患者可能患有的疾病。根据医学知识，我们的模型可以将数字特征转化为描述性文本特征，从而丰富临床文本的语义信息。此外，我们的模型使用基于注意力的序列生成方法来实现多种疾病的诊断，并更好地利用多种疾病之间的相关信息。F1得分达到 65.65%，优于许多其他方法。适用于多种疾病的准确诊断。