A5093787593- Topic Modeling
- Expert finding and Q&A systems
- Literature Analysis and Criticism
- Biomedical Text Mining and Ontologies
- Artificial Intelligence in Healthcare
- Library Science and Administration
- Information Retrieval and Search Behavior
- Machine Learning in Healthcare
- Spam and Phishing Detection
- Complex Network Analysis Techniques
- Artificial Intelligence in Healthcare and Education
- Misinformation and Its Impacts
Emory University
2024
University of Alabama at Birmingham
2024
<sec> <title>BACKGROUND</title> The increasing use of social media to share lived and living experiences substance presents a unique opportunity obtain information on side-effects, usage patterns, opinions novel psychoactive substances (NPS). However, due the large volume data, obtaining useful insights through natural language processing (NLP) technologies such as models (LLMs) is challenging. </sec> <title>OBJECTIVE</title> To develop retrieval-augmented generation (RAG) architecture for...
Natural Language Processing can be used to identify opioid use disorder in patients from clinical text1. We annotate a corpus of text for mentions concepts associated with unhealthy opiates including concept modifiers such as negation, subject, uncertainty, relation document time and illicit use.
Background: The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers involve regular expression or features weights that are trained independently each modifier. Methods: We develop evaluate multi-task transformer architecture design where learned predicted jointly using the publicly available SemEval 2015 Task 14 corpus new Opioid...
The increasing use of social media to share lived and living experiences substance presents a unique opportunity obtain information on side effects, patterns, opinions novel psychoactive substances. However, due the large volume data, obtaining useful insights through natural language processing technologies such as models is challenging. This paper aims develop retrieval-augmented generation (RAG) architecture for medical question answering pertaining clinicians' queries emerging issues...
The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers involve regular expression or features weights that are trained independently each modifier. We develop evaluate multi-task transformer architecture design where learned predicted jointly using the publicly available SemEval 2015 Task 14 corpus new Opioid Use Disorder (OUD)...
The increasing use of social media to share lived and living experiences substance presents a unique opportunity obtain information on side effects, patterns, opinions novel psychoactive substances. However, due the large volume data, obtaining useful insights through natural language processing technologies such as models is challenging. This paper aims develop retrieval-augmented generation (RAG) architecture for medical question answering pertaining clinicians' queries emerging issues...