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Found 12 papers, 1 papers with code
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval.
How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets
Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases.
An Empirical Study of Sections in Classifying Disease Outbreak Reports
Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet.
Investigating Patient Attitudes Towards the use of Social Media Data to Augment Depression Diagnosis and Treatment: a Qualitative Study
In this paper, we use qualitative research methods to investigate the attitudes of social media users towards the (opt-in) integration of social media data with routine mental health care and diagnosis.
Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study
In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record.
A Corpus Analysis of Social Connections and Social Isolation in Adolescents Suffering from Depressive Disorders
Social connection and social isolation are associated with depressive symptoms, particularly in adolescents and young adults, but how these concepts are documented in clinical notes is unknown.
Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e. g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3, 168 features) suggesting there is no consistent count of features for predicting depressive-related tweets.
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression.
Natural Language Processing in Biomedicine: A Unified System Architecture Overview
In modern electronic medical records (EMR) much of the clinically important data - signs and symptoms, symptom severity, disease status, etc.
