使用Transformer网络从成绩单中可解释地识别痴呆
Explainable Identification of Dementia from Transcripts using Transformer Networks
摘要：
阿尔茨海默病（Alzheimer's disease，AD）是痴呆症的主要病因，它伴随着记忆力的丧失，如果不及时诊断，可能会给人们的日常生活带来严重的后果。很少有工程利用变压器为基础的网络，尽管达到了很高的精度，很少的工作已经做了模型的可解释性。此外，虽然简易精神状态检查（MMSE）评分与痴呆的识别有着千丝万缕的联系，但研究工作面临着痴呆识别任务和MMSE评分预测任务两个独立的任务。为了解决这些限制，我们采用了几种基于变压器的模型，BERT达到最高的准确率为87.50%。同时，我们提出了一种可解释的方法来检测AD患者的基础上，达到准确率高达83.75%的连体网络。接下来，我们介绍两个多任务学习模型，其中主任务是指痴呆症的识别（二元分类），而辅助任务对应于痴呆症的严重程度的识别（多类分类）。我们的模型在多任务学习环境中检测AD患者的准确率达到86.25%。最后，我们提出了一些新的方法来识别AD患者和非AD患者使用的语言模式，包括文本统计，词汇的独特性，单词的使用，通过详细的语言分析的相关性，和可解释性技术（LIME）。研究结果表明，AD和非AD患者之间的语言显着差异。
英文摘要：
Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
