Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments
Full-day event, <exact date TBA soon> May 2024, Lingotto Conference Centre, Turin, Italy
Healthcare professionals and clinicians are increasingly inclined to utilize non-invasive, cost-effective, easily measurable techniques, as a complement to the existing array of medical and clinical evaluations, for the early diagnosis or monitoring of brain and mental disorders.
Although many of the causes of cognitive and neuropsychiatric impairments are difficult to foresee and accurately predict, physicians and clinicians work with a wide range of factors that potentially contribute to these impairments, e.g., traumatic brain injuries, genetic predispositions, side effects of medication, and congenital anomalies. In this context, there is new evidence that the acquisition and processing of human language data (e.g., spontaneous story telling) and extra-linguistic and production measures (e.g., from eye tracking, wearable devices or sensors) could be used as a complement to the clinical diagnosis and provide the foundation for future development of objective criteria to be used for identifying progressive decline or degeneration of normal mental and brain functioning.
An important area of research in computational linguistics and Natural Language Processing (NLP) emphasizes the processing, analysis, and interpretation of such data. Current research in this field, based on linguistic-oriented analysis of text and speech produced by such a population, compared to healthy adults, has shown promising outcomes. This is manifested in early diagnosis and prediction of individuals at risk, the differentiation of individuals with various degrees of severity forms of brain and mental illness, sub-typing, and for the monitoring of the progression of such conditions through the longitudinal analysis of language samples or other para and extra-linguistic measurements from various modalities. Furthermore, the development of robust computational tools for e.g., linguistic analysis heavily relies on solid infrastructures that supports the efficient processing and storage of the language data produced. Such infrastructures not only facilitate the advancement of NLP/AI but also play a pivotal role in fostering collaborative research endeavors, enabling the seamless exchange of methodologies and findings across diverse (linguistic) datasets and analytical approaches.
Nevertheless, there remains significant work to be done to arrive at more accurate estimates for prediction and fine-grained classification frameworks and more research is required in order to reliably complement the battery of medical and clinical examinations currently undertaken for the early diagnosis or monitoring of, e.g., neurodegenerative and other brain and mental disorders and accordingly, aid the development of new and in large scale, non-invasive, time and cost-effective and objective (future) clinical tests in neurology, psychology, and psychiatry.