LitSense Insight: Bridging Gaps in Information Retrieval through Sentence Level Knowledge Discovery
Presentation Time: 03:30 PM - 03:45 PM
Abstract Keywords: Information Retrieval, Large Language Models (LLMs), Natural Language Processing, Delivering Health Information and Knowledge to the Public, Deep Learning
Primary Track: Applications
Programmatic Theme: Public Health Informatics
LitSense, a service provided by NCBI, is a web-based system that specializes in biomedical sentence retrieval. Given a query sentence, LitSense retrieves from over 1.3 billion sentences in PubMed abstracts and PMC full texts. In this work, we propose an improvement to LitSense using semantic search technologies powered by MedCPT, a state-of-the-art language embedding model for biomedical text. By incorporating MedCPT embeddings into LitSense, we achieve a significant improvement in retrieval performance.
Speaker(s):
Lana Yeganova, Dr / PhD
NIH
Presentation Time: 03:30 PM - 03:45 PM
Abstract Keywords: Information Retrieval, Large Language Models (LLMs), Natural Language Processing, Delivering Health Information and Knowledge to the Public, Deep Learning
Primary Track: Applications
Programmatic Theme: Public Health Informatics
LitSense, a service provided by NCBI, is a web-based system that specializes in biomedical sentence retrieval. Given a query sentence, LitSense retrieves from over 1.3 billion sentences in PubMed abstracts and PMC full texts. In this work, we propose an improvement to LitSense using semantic search technologies powered by MedCPT, a state-of-the-art language embedding model for biomedical text. By incorporating MedCPT embeddings into LitSense, we achieve a significant improvement in retrieval performance.
Speaker(s):
Lana Yeganova, Dr / PhD
NIH
LitSense Insight: Bridging Gaps in Information Retrieval through Sentence Level Knowledge Discovery
Category
Podium Abstract