Thieu, ThanhCamacho Maldonado, JonathanHo, Pei-ShuCoalition for Advancing Digital Research and Education (2020)2020-06-032020-06-032020-04-17Thieu, T., Camacho Maldonado, J., & Ho, P.-S. (2020, April 17). A comprehensive study of mobility related function in clinical notes. Poster presented at the fourth annual Coalition for Advancing Digital Research and Education (CADRE) Conference, Stillwater, OK.https://hdl.handle.net/20.500.14446/324833Use of free text in Electronic Health Records (EHRs) for clinical, administrative, and research purposes has proliferated in recent years. Using the Mobility domain of the ICF as a framework, we comprehensively analyze the structure and characteristics of mobility related concepts found in physical therapy notes from the National Institutes of Health’s Clinical Center. The result is a mobility entity framework comprised of 5 entities types, 3 relations, 8 attributes, and 33 attribute values. Two domain experts manually curated a gold standard corpus of 14,281 nested entity mentions from 400 clinical notes. Inter-annotator agreement (IAA) of exact matching averaged 92.3% F1-score on mention text spans, and 96.6% Cohen’s kappa on attributes assignments. A novel ensemble machine learning model for named entity recognition was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our ensemble method (83.31%) outperformed both baseline methods: a probabilistic graphical model (80.4%), and an artificial neural network (81.82%). Overcoming the irregularities and challenges in capturing functioning concepts, this work pioneers a representational framework, an annotated gold standard corpus, and a cutting-edge machine learning model that identify concepts in the Mobility domain of the ICF.application/pdfIn the Oklahoma State University Library's institutional repository this paper is made available through the open access principles and the terms of agreement/consent between the author(s) and the publisher. The permission policy on the use, reproduction or distribution of the article falls under fair use for educational, scholarship, and research purposes. Contact Digital Resources and Discovery Services at lib-dls@okstate.edu or 405-744-9161 for further information.Conference proceedingsmobilityfunctional status informationicfwhowhole-person functioninghealth informaticsnatural language processingmachine learning