Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer’s disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.

自然语言处理 (NLP) 是一个不断发展的计算科学领域，旨在对自然人类语言进行建模。结合学习数据模式的机器学习的进步，它提供了包括自动语言分析在内的实用功能。这些方法引起了临床研究人员的兴趣，他们试图了解由于大脑病理变化导致的语言障碍，提供快速、可复制和客观的方法。对阿尔茨海默病 (AD) 和临床前轻度认知障碍 (MCI) 的研究表明，话语（相关的言语或写作）的变化可能是早期发现疾病的关键。AD 是 65 岁以上人群痴呆症的主要原因，目前尚无缓解疾病的治疗方法，但检测那些有患这种疾病风险的人可能有助于识别和测试药物，这些药物可以在潜在疾病发生之前发挥作用。病理已经不可逆转地扩散。我们概述了自然语言的重要组成部分，以及可以提取、分析和用于疾病识别和风险预测的 NLP 工具和方法。我们回顾了使用这些工具对整个 AD 领域的话语进行建模的文献，包括机器学习方法和自动语音识别 (ASR) 的贡献。我们的结论是，自然语言处理和机器学习技术正开始极大地加强该领域的研究，可测量和可量化的语言成分显示出早期检测疾病的希望，但这些方法的临床实施仍然存在研究和实际挑战。讨论的挑战包括大型和多样化数据集的可用性、数据收集和共享的伦理、诊断特异性和临床可接受性。