Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt's direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.

翻译：认知与神经功能损伤非常普遍，但仅有少数受影响个体得到诊断和治疗，部分原因在于频繁筛查的高昂成本。通过高效智能系统检测疾病前兆阶段并分析神经系统疾病进展，有助于实现及时诊断和早期干预。我们提出使用大语言模型从自由对话中提取特征以检测认知衰退。这些特征包含与具体内容无关的高层次推理特征（如理解力下降、觉察力减弱、注意力分散加剧及记忆问题）。我们的解决方案包含：（i）数据预处理，（ii）通过自然语言处理技术与提示工程进行特征构建，（iii）特征分析与选择以优化性能，以及（iv）辅以自动可解释性机制的分类流程。我们还探索了如何利用模型中的最优特征提升Chatgpt直接预测认知损伤的能力。获得的评估指标证实了结合Chatgpt特征提取与专用机器学习模型的混合方法在检测老年人自由形式对话中认知衰退的有效性。最终，我们的工作有望推动开发一种低成本、非侵入性且快速的认知衰退检测与解释手段。