1 code implementation • 15 Mar 2024 • Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen, James Bailey, Henry Pit, Putrasmey Keo, Watey Diep, Yu-Gang Jiang
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval.
no code implementations • 21 Nov 2019 • Son Doan, Amanda Ritchart, Nicholas Perry, Juan D Chaparro, Mike Conway
Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases.
no code implementations • 21 Nov 2019 • Son Doan, Mike Conway, Nigel Collier
Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet.
no code implementations • WS 2017 • Jude Mikal, Samantha Hurst, Mike Conway
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.
no code implementations • WS 2017 • Danielle Mowery, Brett South, Olga Patterson, Shu-Hong Zhu, Mike Conway
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.
no code implementations • WS 2017 • Jia-Wen Guo, Danielle L. Mowery, Djin Lai, Katherine Sward, Mike Conway
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.
no code implementations • 28 Jan 2017 • Danielle Mowery, Craig Bryan, Mike Conway
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.
no code implementations • WS 2016 • Danielle L. Mowery, Albert Park, Craig Bryan, Mike Conway
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.
no code implementations • 3 Jan 2014 • Son Doan, Mike Conway, Tu Minh Phuong, Lucila Ohno-Machado
In modern electronic medical records (EMR) much of the clinically important data - signs and symptoms, symptom severity, disease status, etc.