This paper is in the following e-collection/theme issue:

Electronic Health Records (1303) Natural Language Processing (900) Ontologies, Classifications, and Coding (320) Decision Support for Health Professionals (1520) Clinical Informatics (1309) Machine Learning (2051) Clinical Information and Decision Making (1894)

Published on in Vol 10, No 12 (2022): December

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

Authors of this article:

Laurent Binsfeld Gonçalves1 Author Orcid Image ;   Ivan Nesic1 Author Orcid Image ;   Marko Obradovic1 Author Orcid Image ;   Bram Stieltjes1 Author Orcid Image ;   Thomas Weikert1 Author Orcid Image ;   Jens Bremerich1 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

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  1. Yang T, Sucholutsky I, Jen K, Schonlau M. exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies. PeerJ Computer Science 2024;10:e1888 View
  2. Luo P, Fan C, Li A, Jiang T, Jiang A, Qi C, Gan W, Zhu L, Mou W, Zeng D, Tang B, Xiao M, Chu G, Liang Z, Shen J, Liu Z, Wei T, Cheng Q, Lin A, Chen X. Performance analysis of large language models in multi-disease detection from chest computed tomography reports: a comparative study. International Journal of Surgery 2025;111(8):5071 View

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