A5043622075- Topic Modeling
- Biomedical Text Mining and Ontologies
- Social Media in Health Education
- Misinformation and Its Impacts
- Sentiment Analysis and Opinion Mining
- Data-Driven Disease Surveillance
- Hate Speech and Cyberbullying Detection
- Spam and Phishing Detection
- Mental Health via Writing
- Pharmacovigilance and Adverse Drug Reactions
- Public Relations and Crisis Communication
- Disaster Management and Resilience
- Text Readability and Simplification
- Influenza Virus Research Studies
- Machine Learning in Healthcare
- Seismology and Earthquake Studies
- Academic integrity and plagiarism
- Artificial Intelligence in Healthcare and Education
- COVID-19 diagnosis using AI
- Wikis in Education and Collaboration
- Text and Document Classification Technologies
- Tuberculosis Research and Epidemiology
- Computational and Text Analysis Methods
- Respiratory viral infections research
- Vaccine Coverage and Hesitancy
Mercer University Health Sciences Center
2024
Mercer University
2024
Georgia State University
2019-2023
As the COVID-19 pandemic continues its march around world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups world are releasing publications on ongoing allowing other scientists to learn from local experiences in front lines pandemic. However, there a need integrate additional sources that map measure role social dynamics such unique world-wide event into biomedical, biological, analyses....
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within biomedical community. In PubMed alone, there have nearly 2,500 publication entries since 2014 that deal with analyzing from Twitter and Reddit. However, vast majority those works do not share their code or replicating studies. With minimal exceptions, few do, place burden on researcher to figure out how fetch data, best format create automatic manual annotations acquired data....
With the increase in popularity of deep learning models for natural language processing (NLP) tasks, field Pharmacovigilance, more specifically identification Adverse Drug Reactions (ADRs), there is an inherent need large-scale social-media datasets aimed at such tasks. most researchers allocating large amounts time to crawl Twitter or buying expensive pre-curated datasets, then manually annotating by humans, these approaches do not scale well as and data keeps flowing Twitter. In this work...
As the COVID-19 pandemic continues its march around world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups world are releasing publications on ongoing allowing other scientists to learn from local experiences in front lines pandemic. However, there a need integrate additional sources that map measure role social dynamics such unique world-wide event into biomedical, biological, analyses....
This study presents the outcomes of shared task competition BioCreative VII (Task 3) focusing on extraction medication names from a Twitter user's publicly available tweets (the 'timeline'). In general, detecting health-related is notoriously challenging for natural language processing tools. The main challenge, aside informality used, that people tweet about any and all topics, most their are not related to health. Thus, finding those in timeline mention specific concepts such as...
In the last few years, Twitter has become an important resource for identification of Adverse Drug Reactions (ADRs), monitoring flu trends, and other pharmacovigilance general research applications. Most researchers spend their time crawling Twitter, buying expensive pre-mined datasets, or tediously slowly building datasets using limited API. However, there are a large number that publicly available to underutilized unused. this work, we demonstrate how mined over 9.4 billion Tweets from...
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain virus. Although is no specific antiviral treatment recommended for COVID-19, are several drugs that can potentially help with symptoms. In this work, we mined large twitter dataset 424 million tweets chatter identify discourse around drug mentions. While seemingly straightforward task, due informal nature language use in Twitter, demonstrate need machine learning alongside...
Social media is often utilized as a lifeline for communication during natural disasters. Traditionally, disaster tweets are filtered f rom t he T witter s tream u sing n ame of the and weets re ent or human annotation. The process annotation to create labeled sets machine learning models laborious, time consuming, at times inaccurate, more importantly not scalable in terms size real-time use. In this work, we curated silver standard dataset using weak supervision. order validate its utility,...
Abstract In the last few years Twitter has become an important resource for identification of Adverse Drug Reactions (ADRs), monitoring flu trends, and other pharmacovigilance general research applications. Most researchers spend their time crawling Twitter, buying expensive pre-mined datasets, or tediously slowly building datasets using limited API. However, there are a large number that publicly available to which underutilized unused. this work, we demonstrate how mined over 9.4 billion...
<title>Abstract</title> Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, hybrid approaches. Currently, the development accurate phenotype definitions demands extensive literature reviews clinical experts, rendering process time-consuming inherently unscalable. Large Language Models offer promising avenue for automating definition extraction but come with significant...
Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, hybrid approaches. Currently, the development accurate phenotype definitions demands extensive literature reviews clinical experts, rendering process time-consuming inherently unscalable. Large models offer promising avenue for automating definition extraction but come with significant drawbacks, including reliability...
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain virus. Although is no specific antiviral treatment recommended for COVID-19, are several drugs that can potentially help with symptoms. In this work, we mined large twitter dataset 280 million tweets chatter identify discourse around potential treatments. While seemingly straightforward task, due informal nature language use in Twitter, demonstrate need machine learning methods aid...