Accessibility settings

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

Natural Language Processing (1208) Clinical Information and Decision Making (3452) Ontologies, Classifications, and Coding (404) Artificial Intelligence (4450) Precision Medicine (416)

Published on in Vol 13 (2025)

This is a member publication of University of Manchester (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68558, first published .
Laptop screen displays HPO logo with medical icons: clipboard, heart, stethoscope, pills, eye, nose, graph.

Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation

Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation

Authors of this article:

Areej Alhassan1, 2 Author Orcid Image ;   Viktor Schlegel1, 3 Author Orcid Image ;   Monira Aloud2 Author Orcid Image ;   Riza Batista-Navarro1 Author Orcid Image ;   Goran Nenadic1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Alhassan A, Schlegel V, Cabral R, Batista-Navarro R, Han S, Poon J, Nenadic G. Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis. Frontiers in Digital Health 2026;8 View