Citations 2
The Human Phenotype Ontology in 2024: phenotypes around the world
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Nucleic Acids Research | October 19, 2023 | Volume: 52 | Issue: D1 | Pages: D1333-D1346
Abstract The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)
Liu, Sijia , Wen, Andrew , Wang, Liwei , Fu, Sunyang , Miller, Robert , Williams, Andrew E. , Harris, Daniel R. , Liu, Mei , Abu-el-rub, Noor , Schutte, Dalton, Zhang, Rui , Osborne, John D. , He, Yongqun , Topaloĝlu, Ümit , Hong, Stephanie , Schaffter, Thomas , Fuentes, Rafael , Xu, Hua , He, Huan , Fu, Sunyang , Williams, Andrew , Harris, Daniel , Kavuluru, Ramakanth , Liu, Mei , Zhang, Rui , Rouhizadeh, Masoud , Osborne, John D , He, Yongqun , Topaloglu, Umit , Saltz, Joel H , Schaffter, Thomas , Pfaff, Emily , Chute, Christopher G , Haendel, Melissa , Szolovits, Peter , Xu, Hua , Liu, Hongfang , Liu, Sijia , Wen, Andrew , Abu-El-Rub, Noor , He, Huan , Saltz, Joel , Chute, Christopher G. , Duong, Timothy Q. , Haendel, Melissa , Liu, Hongfang
Journal of the American Medical Informatics Association | August 8, 2023 | Volume: 30 | Issue: 12 | Pages: 2036-2040
Abstract Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.