The entire collection of 11.5 million MEDLINE abstracts was processed to extract 549 million noun phrases using a shallow syntactic parser. English language strings in the 2002 and 2001 releases of the UMLS Metathesaurus were then matched against these phrases using flexible matching techniques. 34% of the Metathesaurus names (occurring in 30% of the concepts) were found in the titles and abstracts of articles in the literature. The matching concepts are fairly evenly chemical and non-chemical in nature and span a wide spectrum of semantic types. This paper details the approach taken and the results of the analysis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244184 | PMC |
J Biomed Inform
January 2025
School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058 China; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. Electronic address:
Objective: Current studies leveraging social media data for disease monitoring face challenges like noisy colloquial language and insufficient tracking of user disease progression in longitudinal data settings. This study aims to develop a pipeline for collecting, cleaning, and analyzing large-scale longitudinal social media data for disease monitoring, with a focus on COVID-19 pandemic.
Materials And Methods: This pipeline initiates by screening COVID-19 cases from tweets spanning February 1, 2020, to April 30, 2022.
J Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212, United States.
Objective: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.
Materials And Methods: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis.
Int J Med Inform
December 2024
Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, the Netherlands; Methodology, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
Introduction: The World Health Organization global standard for representing drug data is the Anatomical Therapeutic Chemical (ATC) classification. However, it does not represent ingredients and other drug properties required by clinical decision support systems. A mapping to a terminology system that contains this information, like RxNorm, may help fill this gap.
View Article and Find Full Text PDFJAMIA Open
February 2025
Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam 14482, Germany.
Objective: To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.
Materials And Methods: We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation.
Medicine (Baltimore)
December 2024
Korean Medicine Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
In traditional medicine (TM), blood stasis syndrome (BSS) is characterized by insufficient blood flow, resulting in a group of symptoms such as fixed pain, a dark complexion, bleeding, and an astringent pulse. While BSS pathology has been previously explored, its molecular mechanisms remain elusive owing to challenges in linking TM symptoms to genes. Our study aimed to elucidate the mechanisms underlying BSS using a phenotype-genotype association approach.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!