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Found 13 papers, 5 papers with code
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets
The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole.
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner.
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
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 deep phenotyping.
Identifying epidemic related Tweets using noisy learning
Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models.
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
Social media is often utilized as a lifeline for communication during natural disasters.
A Biomedically oriented automatically annotated Twitter COVID-19 Dataset
As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes.
Characterizing drug mentions in COVID-19 Twitter Chatter
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus.
GLEAKE: Global and Local Embedding Automatic Keyphrase Extraction
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 are growing in the last few years.
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
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.
Social Media Mining Toolkit (SMMT)
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community.
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
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 Adverse Drug Reactions (ADRs), there is an inherent need for large-scale social-media datasets aimed at such tasks.
Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation
The increasingly available large amounts of solar image data generated by the Solar Dynamic Observatory (SDO) mission make this domain particularly interesting for the development and testing of deep learning systems.
