基于Transformer的多标签序列分类的自动差异诊断

Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification

August 28, 2024

作者: Abu Adnan Sadi, Mohammad Ashrafuzzaman Khan, Lubaba Binte Saber

cs.AI

摘要

随着人工智能领域的发展，辅助技术在各行各业中被广泛应用。医疗保健行业也不例外，有许多研究致力于开发辅助工具，以帮助医疗专业人员。自动诊断系统是其中一种有益的工具，可协助完成多项任务，包括搜集患者信息、分析检测结果和诊断患者。然而，在大多数研究中，开发能够提供不同诊断的系统的想法大多被忽视。在本研究中，我们提出了一种基于Transformer的方法，根据患者的年龄、性别、病史和症状提供不同诊断。我们使用DDXPlus数据集，该数据集根据49种疾病类型为患者提供不同诊断信息。首先，我们提出了一种处理数据集中表格化患者数据并将其转换为患者报告的方法，以使其适用于我们的研究。此外，我们引入了两个数据修改模块，以使训练数据多样化，从而提高模型的鲁棒性。我们将这一任务视为多标签分类问题，并使用四种Transformer模型进行了广泛实验。所有模型在留出测试集上均取得了令人满意的结果，F1分数均超过了97%。此外，我们设计了额外的行为测试，以更全面地了解模型。特别是，在我们的一个测试案例中，我们在一位医生的协助下准备了一个包含100个样本的自定义测试集。自定义集上的结果显示，我们提出的数据修改模块提高了模型的泛化能力。我们希望我们的研究结果能为未来的研究人员提供宝贵的见解，并激励他们开发可靠的自动不同诊断系统。

English

As the field of artificial intelligence progresses, assistive technologies are becoming more widely used across all industries. The healthcare industry is no different, with numerous studies being done to develop assistive tools for healthcare professionals. Automatic diagnostic systems are one such beneficial tool that can assist with a variety of tasks, including collecting patient information, analyzing test results, and diagnosing patients. However, the idea of developing systems that can provide a differential diagnosis has been largely overlooked in most of these research studies. In this study, we propose a transformer-based approach for providing differential diagnoses based on a patient's age, sex, medical history, and symptoms. We use the DDXPlus dataset, which provides differential diagnosis information for patients based on 49 disease types. Firstly, we propose a method to process the tabular patient data from the dataset and engineer them into patient reports to make them suitable for our research. In addition, we introduce two data modification modules to diversify the training data and consequently improve the robustness of the models. We approach the task as a multi-label classification problem and conduct extensive experiments using four transformer models. All the models displayed promising results by achieving over 97% F1 score on the held-out test set. Moreover, we design additional behavioral tests to get a broader understanding of the models. In particular, for one of our test cases, we prepared a custom test set of 100 samples with the assistance of a doctor. The results on the custom set showed that our proposed data modification modules improved the model's generalization capabilities. We hope our findings will provide future researchers with valuable insights and inspire them to develop reliable systems for automatic differential diagnosis.