311 citations · 408 across the 13 of their papers we have counts for
14 papers
Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets
Ramya Tekumalla, Juan M. Banda
The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole. With the rapid development of COVID-19 vaccines, social media platform…
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Debadutta Dash, Rahul Thapa, Juan M. Banda +15
Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our…
Identifying epidemic related Tweets using noisy learning
Ramya Tekumalla, Juan M. Banda
Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models. However, the curation of the annotated datasets is laborious and time con…
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa +29
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for dee…
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
Ramya Tekumalla, Juan M. Banda
Social media is often utilized as a lifeline for communication during natural disasters. Traditionally, natural disaster tweets are filtered from the Twitter stream using the name…
A Biomedically oriented automatically annotated Twitter COVID-19 Dataset
Luis Alberto Robles Hernandez, Tiffany J. Callahan, Juan M. Banda
The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontra…
Characterizing drug mentions in COVID-19 Twitter Chatter
Ramya Tekumalla, Juan M. Banda
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommend…
GLEAKE: Global and Local Embedding Automatic Keyphrase Extraction
Javad Rafiei Asl, Juan M. Banda
Automated methods for granular categorization of large corpora of text documents have become increasingly more important with the rate scientific, news, medical, and web documents…
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
Juan M. Banda, Ramya Tekumalla, Guanyu Wang +6
As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled r…
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
Ramya Tekumalla, Juan M. Banda
With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of…
Social Media Mining Toolkit (SMMT)
Ramya Tekumalla, Juan M. Banda
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2…
Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation
Toqi Tahamid Sarker, Juan M. Banda
With the advent of deep learning for computer vision tasks, the need for accurately labeled data in large volumes is vital for any application. The increasingly available large amo…
Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data
Tobias Kuhn, Albert Meroño-Peñuela, Alexander Malic +12
Nanopublications are a Linked Data format for scholarly data publishing that has received considerable uptake in the last few years. In contrast to the common Linked Data publishin…
Provenance-Centered Dataset of Drug-Drug Interactions
Juan M. Banda, Tobias Kuhn, Nigam H. Shah +1
Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records,…