ArticleBioMed research international2017
Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Computable longitudinal patient journeys from structured and unstructured EHR data.Nature medicine · 2026Article
- AutoPCR: automated phenotype concept recognition by prompting.Bioinformatics (Oxford, England) · 2026Article
- Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis.Frontiers in digital health · 2026Article
- Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation.JMIR medical informatics · 2025Article
- FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology.Bioinformatics (Oxford, England) · 2024Article
- An evaluation of GPT models for phenotype concept recognition.BMC medical informatics and decision making · 2024Article
- TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature.PloS one · 2024Article
- Term-BLAST-like alignment tool for concept recognition in noisy clinical texts.Bioinformatics (Oxford, England) · 2023Article
- PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.Journal of biomedical informatics · 2022Article
- PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.Bioinformatics (Oxford, England) · 2021Article
- Improved characterisation of clinical text through ontology-based vocabulary expansion.Journal of biomedical semantics · 2021Article
- A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.PLoS genetics · 2020Article
- PPR-SSM: personalized PageRank and semantic similarity measures for entity linking.BMC bioinformatics · 2019Article
- Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning.JMIR medical informatics · 2019Article
- MER: a shell script and annotation server for minimal named entity recognition and linking.Journal of cheminformatics · 2018Article
- Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.PloS one · 2018Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO) is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP) system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier. However, the main novelty of IHP is its validation step based on a set of carefully crafted manual rules, such as the negative connotation analysis, that combined with a dictionary can filter incorrectly identified entities, find missed entities, and combine adjacent entities. The performance of IHP was evaluated using the recently published HPO Gold Standardized Corpora (GSC), where the system Bio-LarK CR obtained the best
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