846 research outputs found
Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function
Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders
Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function
Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which
computational analyses align best with the targeted neurocognitive/psychological functions that we want to
assess. In this paper we reflect on two decades of experience with the application of language-based assessment
to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it
should be measured and why we are measuring the phenomena. We address the questions by advocating for a
principled framework for aligning computational models to the constructs being assessed and the tasks being
used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the
accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled
approach can further the goal of transitioning language-based computational assessments to part of clinical
practice while gaining the trust of critical stakeholders
Predictive Analysis on Twitter: Techniques and Applications
Predictive analysis of social media data has attracted considerable attention
from the research community as well as the business world because of the
essential and actionable information it can provide. Over the years, extensive
experimentation and analysis for insights have been carried out using Twitter
data in various domains such as healthcare, public health, politics, social
sciences, and demographics. In this chapter, we discuss techniques, approaches
and state-of-the-art applications of predictive analysis of Twitter data.
Specifically, we present fine-grained analysis involving aspects such as
sentiment, emotion, and the use of domain knowledge in the coarse-grained
analysis of Twitter data for making decisions and taking actions, and relate a
few success stories
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
DrugExBERT for Pharmacovigilance â A Novel Approach for Detecting Drug Experiences from User-Generated Content
Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data- and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
One of the major hurdles preventing the full exploitation of information from
online communities is the widespread concern regarding the quality and
credibility of user-contributed content. Prior works in this domain operate on
a static snapshot of the community, making strong assumptions about the
structure of the data (e.g., relational tables), or consider only shallow
features for text classification.
To address the above limitations, we propose probabilistic graphical models
that can leverage the joint interplay between multiple factors in online
communities --- like user interactions, community dynamics, and textual content
--- to automatically assess the credibility of user-contributed online content,
and the expertise of users and their evolution with user-interpretable
explanation. To this end, we devise new models based on Conditional Random
Fields for different settings like incorporating partial expert knowledge for
semi-supervised learning, and handling discrete labels as well as numeric
ratings for fine-grained analysis. This enables applications such as extracting
reliable side-effects of drugs from user-contributed posts in healthforums, and
identifying credible content in news communities.
Online communities are dynamic, as users join and leave, adapt to evolving
trends, and mature over time. To capture this dynamics, we propose generative
models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian
Motion to trace the continuous evolution of user expertise and their language
model over time. This allows us to identify expert users and credible content
jointly over time, improving state-of-the-art recommender systems by explicitly
considering the maturity of users. This also enables applications such as
identifying helpful product reviews, and detecting fake and anomalous reviews
with limited information.Comment: PhD thesis, Mar 201
The Personality Panorama:Conceptualizing Personality Through Big Behavioural Data
Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant-report, and self-report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self-report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a âpersonality panopticonâ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are
leading contributors to the global burden of disease and intensely influence
social and financial welfare of individuals. The present comprehensive review
concentrate on the two mental disorders: Major depressive Disorder (MDD) and
Bipolar Disorder (BD) with noteworthy publications during the last ten years.
There is a big need nowadays for phenotypic characterization of psychiatric
disorders with biomarkers. Electroencephalography (EEG) signals could offer a
rich signature for MDD and BD and then they could improve understanding of
pathophysiological mechanisms underling these mental disorders. In this review,
we focus on the literature works adopting neural networks fed by EEG signals.
Among those studies using EEG and neural networks, we have discussed a variety
of EEG based protocols, biomarkers and public datasets for depression and
bipolar disorder detection. We conclude with a discussion and valuable
recommendations that will help to improve the reliability of developed models
and for more accurate and more deterministic computational intelligence based
systems in psychiatry. This review will prove to be a structured and valuable
initial point for the researchers working on depression and bipolar disorders
recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table
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