BibTeX

@misc{kokkinakis-2016-proceedings-252412,
	title        = {Proceedings of LREC 2016 Workshop: Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016. Linköping electronic conference proceedings.},
	abstract     = {The purpose of the Workshop on “Resources and ProcessIng of linguistic and extra-linguistic Data
from people with various forms of cognitive/psychiatric impairments” (RaPID-2016) was to provide
a snapshot view of some of the current technological landscape, resources, data samples and also
needs and challenges in the area of processing various data from individuals with various types of
mental and neurological health impairments and similar conditions at various stages; increase the
knowledge, understanding, awareness and ability to achieve useful outcomes in this area and
strengthen the collaboration between researchers and workers in the field of clinical/nursing/medical
sciences and those in the field of language technology/computational linguistics/Natural Language
Processing (NLP).
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 such 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 linguistic data (e.g., spontaneous story telling) and extra-linguistic and production
measures (e.g., eye tracking) could be used as a complement to 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 new area of research in NLP emphasizes the processing, analysis, and interpretation of
such data and current research in this field, based on linguistic-oriented analysis of text and speech
produced by such a population and 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, and for the monitoring of the
progression of such conditions through the diachronic analysis of language samples or other extralinguistic
measurements. Initially, work was based on written data but there is a rapidly growing body
of research based on spoken samples and other modalities.
Nevertheless, there remains significant work to be done to arrive at more accurate estimates for
prediction purposes in the future 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, non-invasive, time and cost-effective and objective (future) clinical tests in
neurology, psychology, and psychiatry.},
	author       = {Kokkinakis, Dimitrios},
	year         = {2016},
	publisher    = {Linköping University Electronic Press},
	address      = {Linköping},
	ISBN         = {978-91-7685-730-4},
}

@inProceedings{kokkinakis-etal-2016-data-243069,
	title        = {Data Resource Acquisition from People at Various Stages of Cognitive Decline – Design and Exploration Considerations},
	abstract     = {In this paper we are introducing work in progress towards the development of an infrastructure (i.e., design, methodology, creation and description) of linguistic and extra-linguistic data samples acquired from people diagnosed with subjective or mild cognitive impairment and healthy, age-matched controls. The data we are currently collecting consists of various types of modalities; i.e. audio-recorded spoken language samples; transcripts of the audio recordings (text) and eye tracking measurements. The integration of the extra-linguistic information with the linguistic phenotypes and measurements elicited from audio and text, will be used to extract, evaluate and model features to be used in machine learning experiments. In these experiments, classification models that will be trained, that will be able to learn from the whole or a subset of the data to make predictions on new data in order to test how well a differentiation between the aforementioned groups can be made. Features will be also correlated with measured outcomes from e.g. language-related scores, such as word fluency, in order to investigate whether there are relationships between various variables.},
	booktitle    = {The Seventh International Workshop on Health Text Mining and Information Analysis (Louhi). November 5, 2016, Austin, Texas, USA},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Nordlund, Arto},
	year         = {2016},
}

@inProceedings{kokkinakis-2016-linguistic-243100,
	title        = {Linguistic and extra-linguistic parameters for early detection of cognitive impairment},
	abstract     = {AIM: to adapt, develop and test methods that in isolation have shown promising outcomes on tasks related to (early) detection of dementia, differentiating between various dementia types and controls and also increase our understanding of the cognitive processes that underlie written text and certain forms of spoken language production. Unlike previous models, based solely on a certain aspect of language abilities (i.e. on written or spoken language alone), the project is comprehensive and more likely to provide new insights in the area of dementia detection and improve practices applied so far. The project builds on the success stories of the past and focus on the interplay between various types of technologies that hold the potential to provide reliable estimates for the detection of cognitive decline. The project emphasizes its interdisciplinary nature, by bringing together researchers from humanities (computational linguistics / language technology), computer science and medicine, and foresees the development of a comprehensive set of novel analytic approaches not explored jointly in the past
GOAL: discovering evidence about linguistic performance and identifying whether the addition of new ways for investigating, combining and evaluating measurement and other parameters for improvement of established models can advance our understanding of: i) the boundaries between normal aging and dementia; ii) its effects on linguistic performance extrapolated from various sources and iii) whether effects of cognitive decline can be seen across (daily) language production.
},
	booktitle    = {European Summer School on Eye Movements (ESSEM),   11-17 september, 2016  Athens, Greece.},
	author       = {Kokkinakis, Dimitrios},
	year         = {2016},
}

@inProceedings{kokkinakis-etal-2016-specifications-243183,
	title        = {Specifications and Methodology for Language-Related Data Acquisition and Analysis in the Domain of Dementia Diagnostics},
	abstract     = {This paper outlines the initial stages of a project that aims to build and use a corpus with data samples acquired from people diagnosed with subjective or mild cognitive impairment and healthy, age-matched controls. The data we are currently collecting consists of audio-recorded spoken language samples; transcripts of the audio recordings and eye tracking measurements. From these data we plan to extract, evaluate and model features to be used for learning classification models in order to test how well a differentiation between the aforementioned subject groups can be made. Features will be also correlated with outcomes from e.g. other language-related scores, such as word fluency, in order to investigate whether there are relationships between various variables.},
	booktitle    = { The Sixth Swedish Language Technology Conference (SLTC) Umeå University, 17-18 November, 2016},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Björkner, Eva and Nordlund, Arto},
	year         = {2016},
}