NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 20, 2026 Anastasia Gulyaeva
TBD -
Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 27, 2026 Zhaohui Liang
TBD -
Jan. 29, 2026 Mehdi Bagheri Hamaneh
FastSpel: A simple peptide spectrum predictor that achieves deep learning-level performance at a fraction of the computational cost -
Feb. 3, 2026 Matthew Diller
TBD
RECENT SEMINARS
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Jan. 8, 2026 Won Gyu Kim
LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery -
Dec. 16, 2025 Sarvesh Soni
ArchEHR-QA: A Dataset and Shared Task for Grounded Question Answering from Electronic Health Records -
Dec. 2, 2025 Qingqing Zhu
CT-Bench & CARE-CT: Building Reliable Multimodal AI for Lesion Analysis in Computed Tomography -
Nov. 25, 2025 Jing Wang
MIMIC-EXT-TE: Millions Clinical Temporal Event Time-Series Dataset -
Oct. 21, 2025 Yifan Yang
TBD
Scheduled Seminars on Aug. 16, 2022
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
UMLS Metathesaurus is a biomedical terminology integration system that integrates biomedical vocabularies by grouping synonymous terms into concepts. The construction and maintenance process of the UMLS Metathesaurus mainly relies on (1) the lexical and semantic processing for suggesting the groupings of synonymous terms and (2) the expertise of the UMLS editors for curating these synonymy suggestions. With the enormous knowledge accumulated over 30 years of manual curation, the existing Metathesaurus provides ample material for deep learning approaches, which have gained promising results especially in the field of natural language processing in recent years. A UMLS learning framework has been developed to explore and study various aspects of applying deep learning techniques to the vocabulary alignment task in the UMLS Metathesaurus.