Automatic diagnosis (AD), a critical application of AI in healthcare, employs
machine learning techniques to assist doctors in gathering patient symptom
information for precise disease diagnosis. The Transformer-based method
utilizes an input symptom sequence, predicts itself through auto-regression,
and employs the hidden state of the final symptom to determine the disease.
Despite its simplicity and superior performance demonstrated, a decline in
disease diagnosis accuracy is observed caused by 1) a mismatch between symptoms
observed during training and generation, and 2) the effect of different symptom
orders on disease prediction. To address the above obstacles, we introduce the
CoAD, a novel disease and symptom collaborative generation framework, which
incorporates several key innovations to improve AD: 1) aligning sentence-level
disease labels with multiple possible symptom inquiry steps to bridge the gap
between training and generation; 2) expanding symptom labels for each
sub-sequence of symptoms to enhance annotation and eliminate the effect of
symptom order; 3) developing a repeated symptom input schema to effectively and
efficiently learn the expanded disease and symptom labels. We evaluate the CoAD
framework using four datasets, including three public and one private, and
demonstrate that it achieves an average 2.3% improvement over previous
state-of-the-art results in automatic disease diagnosis. For reproducibility,
we release the code and data at this https URL

引入了一个名为 CoAD 的新型疾病和症状协作生成框架，通过对多个关键创新的整合来改善 AD，并在各类数据集上展示得到了平均 2.3% 的改进。