DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP

DeconDTN：为临床 NLP 解构深度 Transformer 网络

• 批准号: 10711315
• 负责人: Trevor Cohen
• 金额: $ 31.12万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10711315
• 关键词: Acceleration; Adverse effects; Affect; African American; African American population; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Award; Caregivers; Caring; Classification; Clinical; Cognitive; Confounding Factors (Epidemiology); Data; Data Provenance; Data Set; Dementia; Detection; Diagnosis; Disease; Early Diagnosis; Elderly; Emotional; Equity; Ethnic Origin; Ethnic Population; Family; Family Relationship; Financial Hardship; Future; Genetic Transcription; Health Care Costs; Healthcare Systems; Hispanic Americans; Individual; Inequity; Institution; Language; Learning; Linguistics; Measures; Mediating; Methods; Modeling; Modification; Monitor; Natural Language Processing; Nature; Neural Network Simulation; Neurobehavioral Manifestations; Outcome; Parents; Participant; Patients; Performance; Personality; Persons; Population; Prognosis; Race; Research; Sampling; Semantics; Series; Site; Social isolation; Societies; Speech; Structure; Symptoms; Text; Time; Training; Transcript; Uncertainty; Underrepresented Populations; Variant; Work; automated speech recognition; cognitive change; cognitive function; cognitive task; cost; cost effective; deep learning; deep learning model; detection method; ethnic disparity; improved; machine learning classifier; machine learning model; marginalization; neural network architecture; neural network classifier; racial disparity; racial population; response; sound; tool

Abstract This proposal relates to ongoing efforts to develop automated methods for the detection of linguistic manifestations of cognitive changes in Alzheimer’s Disease (AD). These methods have the potential to alleviate the personal and societal burden of AD, by reducing time to diagnosis. Delayed AD diagnosis has adverse effects on care planning and family relationships, and has been estimated to cost the healthcare system close to $8 trillion dollars. Language reflects cognitive status, and contemporary neural network models have been shown to discriminate between transcribed speech from patients with AD and that from healthy controls with promising accuracy. However, most of this work has been conducted in the context of recorded responses to picture description tasks, which are not suitable for repeated, continuous or passive monitoring for linguistic indicators of AD-related decline. In contrast, our recent work has identified the task-agnostic linguistic construct of semantic coherence as a sound basis for machine learning models for detection of language from people with AD in casual conversations, with classification performance in this context exceeding that of Bidirectional Encoder Representations from Transformers (BERT) based classifiers, the state-of-the-art for text-based AD detection. Unfortunately, recent work has shown that automated coherence estimates are vulnerable to bias, with lower estimates for speech from people identifying as black irrespective of diagnosis. Interrogation of coherence- and BERT-based classifiers reveals they dramatically underperform in this group also. For the parent award for this supplement proposal (R01 LM0104056), we will develop methods to debias deep transformer networks to mitigate for the confounding variable of provenance in multi-institutional datasets, culminating in the release of a toolkit to Deconfound Deep Transformer Networks, the DeconDTN suite. In this proposal we will apply these methods to deconfound coherence- and BERT-based AD detection models for the confounding variable of race and/or ethnicity, and evaluate the effects on model performance across groups. In addition, we will evaluate the utility of fine-tuning semantic coherence models and models used to generate automated transcripts on text and speech from the recently-released Corpus of Regional African American Language (CORAAL). We hypothesize that both improvements in these underlying models, and explicit deconfounding for race/ethnicity will reduce the performance differential across groups, resulting in equitable models for language-based AD detection.

摘要 这项建议涉及正在进行的开发自动化方法的努力， 阿尔茨海默病（AD）的认知变化的表现。这些方法有可能缓解 AD的个人和社会负担，通过缩短诊断时间。延迟AD诊断有不良影响 在护理计划和家庭关系方面，据估计，医疗保健系统的成本接近8美元。 万亿美元。语言反映认知状态，当代神经网络模型已显示 区分AD患者和健康对照者的转录语音， 精度然而，这项工作的大部分是在记录对图片的反应的背景下进行的。 描述任务，不适合重复、连续或被动监测语言指标 与AD相关的衰退相比之下，我们最近的工作已经确定了语义的任务不可知的语言结构， 连贯性作为机器学习模型的坚实基础，用于检测AD患者的语言， 会话，在这种情况下的分类性能超过了双向编码器 基于分类器的变形金刚表示（BERT），是基于文本的AD检测的最新技术。 不幸的是，最近的工作表明，自动一致性估计容易受到偏见的影响， 估计的语音从人确定为黑色的诊断无关。对连贯性的质疑 基于BERT的分类器显示，它们在这一组中也表现不佳。因为这件事获得了家长奖 补充提案（R 01 LM 0104056），我们将开发深度Transformer网络去偏置的方法， 缓解多机构数据集中来源的混淆变量，最终发布 Deconfound Deep Transformer Networks的工具包，DeconDTN套件。在本提案中，我们将应用这些 用于种族混杂变量的基于相干性和BERT的AD检测模型的去发现方法 和/或种族，并评估对各组模型性能的影响。此外，我们将评估 微调语义连贯性模型和用于生成文本自动记录的模型的效用， 这是一个新的语料库的区域非洲裔美国人语言（CORAAL）。我们假设 这些基本模型的改进以及种族/民族的明确解构将减少 不同组之间的性能差异，从而为基于语言的AD检测提供公平的模型。