Document Type
Article
Publication Date
7-1-2024
Original Citation
Groza T,
Gration D,
Baynam G,
Robinson P.
FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology. Bioinformatics. 2024;40(7):btae406.
Keywords
JGM, Humans, Phenotype, Biological Ontologies, Natural Language Processing, Software, Algorithms
JAX Source
Bioinformatics. 2024;40(7):btae406.
ISSN
1367-4811
PMID
38913850
DOI
https://doi.org/10.1093/bioinformatics/btae406
Grant
This work was supported by NIH NHGRI [1U24HG011449], NIH Office of the Director [2R24OD011883], and the European Union’s Horizon 2020 research and innovation pro- gram [grant agreement No. 779257] (SOLVE-RD) to P.N.R.;
Abstract
MOTIVATION: Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing adoption of large language models (LLMs) for natural language understanding has led to several LLM-based solutions, we argue that their intrinsic resource-intensive nature is not suitable for realistic management of the phenotype CR lifecycle. Consequently, we propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate refresh of the ontological concepts as well as efficient re-analysis of past data.
RESULTS: We developed a dictionary-based approach using a pre-built large collection of clusters of morphologically equivalent tokens-to address lexical variability and a more effective CR step by reducing the entity boundary detection strictly to candidates consisting of tokens belonging to ontology concepts. Our method achieves state-of-the-art results (0.76 F1 on the GSC+ corpus) and a processing efficiency of 10 000 publication abstracts in 5 s.
AVAILABILITY AND IMPLEMENTATION: FastHPOCR is available as a Python package installable via pip. The source code is available at https://github.com/tudorgroza/fast_hpo_cr. A Java implementation of FastHPOCR will be made available as part of the Fenominal Java library available at https://github.com/monarch-initiative/fenominal. The up-to-date GCS-2024 corpus is available at https://github.com/tudorgroza/code-for-papers/tree/main/gsc-2024.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.