Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce non-grammatical speech that requires manual or high precision automatic correction of transcripts. In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments. The network measurements were applied with well-known classifiers to automatically identify MCI in transcripts, in a binary classification task. A comparison was made with the performance of traditional approaches using Bag of Words (BoW) and linguistic features for three datasets: DementiaBank in English, and Cinderella and Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using only complex networks, while Support Vector Machine was superior to other classifiers. CNE provided the highest accuracies for DementiaBank and Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably owing to its short narratives. The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised. Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments
Multilingual Cognitive Impairment Detection in the Era of Foundation Models
Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University（协同与心理治疗研究所，帕拉塞尔苏斯医科大学）；University of Ljubljana（卢布尔雅那大学）；Queen Mary University of London（伦敦玛丽女王大学）
Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks
Interinstitutional Center for Computational Linguistics (NILC)（跨机构计算语言学中心）；Institute of Mathematical and Computer Sciences（数学与计算机科学研究所）；University of São Paulo（圣保罗大学）
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss
Ritchie School of Engineering and Computer Science（工程与计算机科学里奇学院）；DreamFace Technologies LLC（DreamFace科技有限责任公司）；Massachusetts General Hospital（麻省总医院）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）
Institute of Health Informatics, University of Minnesota（明尼苏达大学健康信息学研究所）；Biomedical Informatics and Medical Education, University of Washington（华盛顿大学生物医学信息学与医学教育系）；Department of Pharmaceutical Care & Health Systems, University of Minnesota（明尼苏达大学药物护理与卫生系统系）
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
Institute of Health Informatics, University of Minnesota（明尼苏达大学健康信息学研究所）；Biomedical Informatics and Medical Education, University of Washington（华盛顿大学生物医学信息学与医学教育系）；Department of Pharmaceutical Care & Health Systems, University of Minnesota（明尼苏达大学药物护理与卫生系统系）
LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；University of Tehran（德黑兰大学）；Columbia University（哥伦比亚大学）；University of Florida（佛罗里达大学）
Explainable and Generalisable LLM-based Cognitive Decline Detection with Spontaneous Speech
Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University Sixth Hospital（北京大学第六医院）；Peking University（北京大学）；OPPO Research Institute（OPPO研究院）；Peking University Third Hospital（北京大学第三医院）
