Natural language processing to build a computable phenotype library for adults with congenital heart disease
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
OBJECTIVE: Our objective was to build classifiers for multiple phenotypes that categorize a cohort of adults with congenital heart disease (ACHD), that can be used to populate variables in a biobank. MATERIALS AND METHODS: A dataset of 1492 ACHD patients, with expert-created labels for eight phenotypes, was created and used to train classifiers with three different architectures. A larger unlabeled dataset containing 15,869 patients was used to pre-train the classifiers, and a 20 % subset of the unlabeled dataset was used to validate the classifier predictions. RESULTS: On held out labeled data, F1 scores for the eight target phenotypes of interest ranged from 0.66 to 1. Of those, the six phenotypes with best classification performance were then validated on unlabeled data, where positive predictive value ranged from 81.5 % to 100 %. DISCUSSION: We were able to classify six out of eight phenotypes with satisfactory performance. Challenging phenotypes included cyanosis and New York Heart Association functional class. Both vary over time and in the latter case there is limited agreement between human observers. Different phenotypes benefited from different model architectures to some degree, but the differences are small enough that uniformity of deployment may be a more important factor in choosing what models to deploy. We saw no benefit to joint training, but some phenotypes benefited from a multiclass model. CONCLUSION: Human-curated data can be used to train text-based ACHD phenotype classifiers with promising internal performance acceptable for application in quality improvement efforts and to populate ACHD registry data.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Natural language processing to build a computable phenotype library for adults with congenital heart disease
- Date Crossref
- 01/11/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Boston Children's Hospital Computational Health Informatics Program pays non établi dans la noticeÉtablissement de santé
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Johns Hopkins University Division of General Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
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Johns Hopkins Medicine pays non établi dans la noticeÉtablissement de santé
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Cincinnati Children's Hospital Medical Center pays non établi dans la noticeÉtablissement de santé
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University of Cincinnati Medical Center Department of Biostatistics pays non établi dans la noticeÉtablissement de santé
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Vanderbilt University Medical Center pays non établi dans la noticeÉtablissement de santé
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Cincinnati Health Department pays non établi dans la noticeOrganisme public
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University of Cincinnati Heart Institute pays non établi dans la noticeUniversité ou école supérieure
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University of North Carolina at Chapel Hill pays non établi dans la noticeUniversité ou école supérieure
Computational Health Informatics Program — Boston Children's Hospital, Division of General Internal Medicine — Johns Hopkins University et Johns Hopkins Medicine, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.