使用基於Transformer的多標籤序列分類進行自動差異診斷

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

隨著人工智慧領域的進展，輔助技術在各行業中被廣泛應用。醫療保健行業也不例外，許多研究正在進行以開發醫療專業人員使用的輔助工具。自動診斷系統是其中一種有益的工具，可協助執行多項任務，包括收集病人信息、分析檢驗結果和診斷病人。然而，在大多數研究中，開發能提供差異診斷的系統的概念大多被忽視。在本研究中，我們提出了一種基於變壓器的方法，根據病人的年齡、性別、病史和症狀提供差異診斷。我們使用了DDXPlus數據集，該數據集根據49種疾病類型為病人提供差異診斷信息。首先，我們提出了一種方法來處理數據集中的表格狀病人數據，並將其轉換為病人報告，以使其適合我們的研究。此外，我們引入了兩個數據修改模塊來使訓練數據多樣化，從而提高模型的韌性。我們將此任務視為多標籤分類問題，並使用四個變壓器模型進行了廣泛實驗。所有模型在留出測試集上均取得了令人期待的結果，F1分數均超過了97%。此外，我們設計了額外的行為測試，以更廣泛地了解這些模型。特別是，在我們的一個測試案例中，我們在一位醫生的協助下準備了一個包含100個樣本的自定義測試集。在自定義集上的結果顯示，我們提出的數據修改模塊提高了模型的泛化能力。我們希望我們的研究結果能為未來的研究人員提供寶貴的見解，並激勵他們開發可靠的自動差異診斷系統。

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.