@inproceedings{77d67c7e1c334d3fa80a06d0c150f086,
title = "Phenotypic analysis of clinical narratives using human phenotype ontology",
abstract = "Phenotypes are defined as observable characteristics and clinical traits of diseases and organisms. As connectors between medical experimental findings and clinical practices, phenotypes play vital roles in translational medicine. To facilitate the translation between genotype and phenotype, Human Phenotype Ontology (HPO) was developed as a semantically computable vocabulary to capture phenotypic abnormalities found in human diseases discovered through biomedical research. The use of HPO in annotating phenotypic information in clinical practice remains unexplored. In this study, we investigated the use of HPO to annotate phenotypic information in clinical domain by leveraging a corpus of 12.8 million clinical notes created from 2010 to 2015 for 729 thousand patients at Mayo Clinic Rochester campus and assessed the distribution information of HPO terms in the corpus. We also analyzed the distributional difference of HPO terms among demographic groups. We further demonstrated the potential application of the annotated corpus to support knowledge discovery in precision medicine through Wilson's Disease.",
keywords = "Human phenotype ontology, Phenotypic analysis, Semantic annotation",
author = "Feichen Shen and Liwei Wang and Hongfang Liu",
note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",
year = "2017",
doi = "10.3233/978-1-61499-830-3-581",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "581--585",
editor = "Zhao Dongsheng and Gundlapalli, {Adi V.} and Jaulent Marie-Christine",
booktitle = "MEDINFO 2017",
}