用聊天机器人实时分析语言，提前预警认知衰退，结果准确且可解释。
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time
- 通过自然语言处理提取语言特征，构建实时预测流程
- 分类准确率超80%，衰退类召回率达85%
- 提供可视化解释界面，帮助医生理解模型决策
据官方估计，全球有5000万痴呆症患者，每年新增1000万。目前尚无治愈方法，临床预判与早期干预是延缓进展最有效手段。人工智能与计算语言学可用于语言分析、个性化评估、监测与治疗。然而传统方法在语义管理与可解释性方面不足。尽管大语言模型（LLMs）是智能系统中实现医患沟通的前沿技术，但其在认知衰退诊断中的应用仍稀少。为此，本研究利用最新NLP技术，构建基于LLM的聊天机器人系统，实现实时、可解释的机器学习预测。通过语言-概念特征进行自然语言分析，结合可解释性机制，以减少模型潜在偏见，提升对临床决策的支持能力。具体流程包括：(i) 基于NLP提示工程的数据提取；(ii) 流式数据处理，含特征工程、分析与选择；(iii) 实时分类；(iv) 可解释性仪表板，提供预测结果的可视化与自然语言描述。所有评估指标分类准确率均超过80%，衰退类召回率约为85%。综上，我们提出一个低成本、灵活、非侵入性、个性化的诊断系统。
原文摘要 · Abstract (English)
Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, Artificial Intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using Large Language Models (LLMs) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical-patient communication using intelligent systems. Consequently, we leverage an LLM using the latest Natural Language Processing (NLP) techniques in a chatbot solution to provide interpretable Machine Learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing NLP-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80 % in all evaluation metrics, with a recall value for the mental deterioration class about 85 %. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.
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