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Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria)., Aims: To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach., Methods & Procedures: We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews., Main Contribution: The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders)., Conclusions & Implications: Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice., What This Paper Adds: What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach., (© 2023 The Authors. International Journal of Language & Communication Disorders published by John Wiley & Sons Ltd on behalf of Royal College of Speech and Language Therapists.)

This document is an introduction to a special issue of the journal "Language Resources & Evaluation" focused on language resources for clinical linguistics. Clinical linguistics is an interdisciplinary field that studies the linguistic characteristics associated with medical conditions. The special issue covers a range of topics, including corpus-based research in speech-language pathology, the identification and use of language resources for clinical applications, the design of annotation tools for analyzing atypical verbal productions, and legal and ethical aspects of language technology. The issue includes papers on topics such as dementia, schizophrenia, pragmatic skills, breast cancer diagnosis, depressive symptoms detection on Twitter, and robotic-assisted surgery. The authors provide a brief overview of each paper and express gratitude to the authors and reviewers involved in the special issue. [Extracted from the article]

Background: The discovery of early, non-invasive biomarkers for the identification of "preclinical" or "pre-symptomatic" Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals. Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features. Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI. Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption. [ABSTRACT FROM AUTHOR]

• Pharmacologic treatments for Dementia is limited to symptoms control thus a prompt detection is a key problem. • Linguistic alterations may be one of the earliest signs of Dementia. • Spoken language analysis by NLP techniques can identify minor language modifications in potential patients. • Automatic classifiers are able to distinguish between Italian controls and MCI subjects exhibiting high F1 scores. Almost 50 million people are living with dementia in 2018 worldwide, and the number will double every 20 years. The effectiveness of existing pharmacologic treatments for the disease is limited to symptoms control, and none of them are able to prevent, reverse or turn off the neurodegenerative process that leads to dementia; therefore, a prompt detection of the "disease signature" is a key problem, in order to develop and test new drugs and to support the management of clinical and domestic context. Recent studies showed that linguistic alterations may be one of the earliest signs of the pathology, years before other neurocognitive deficits become evident. Traditional tests fail to identify these slight but noticeable changes; whereas, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can ecologically and inexpensively identify minor language modifications in potential patients. This interdisciplinary study aims at quantifying and describing alterations of linguistic features due to cognitive decline and build an automatic system for early diagnosis and screening purpose. To this aim, we enrolled 96 participants: 48 healthy controls and 48 impaired subjects. Of the latter, 32 was diagnosed with Mild Cognitive Impairment and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening, and samples of semi-spontaneous speech productions was collected by means of three elicitation tasks. Recorded sessions were orthographically transcribed, PoS tagged and parsed building two different corpora: in the first we kept the automatic annotations, while in the second the transcripts were manually corrected in order to remove all mistakes. A multidimensional parameter computation was performed on the data, taking into consideration a set of 87 acoustical, rhythmical, morpho-syntactic and lexical feature as well as some readability indexes and demographic information. After these preparatory steps, some automatic classifiers were trained to distinguish healthy controls from MCI subjects employing two different algorithms, Support Vector (SVC) and Random Forest Classifiers (RFC). Our system was able to distinguish between controls and MCI subjects exhibiting high F1 scores, around 75%, thus it seems to be a promising approach for the identification of preclinical stages of dementia. [ABSTRACT FROM AUTHOR]