Comprehensive study of mobility related function in clinical notes
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Date
2020-04-17Author
Thieu, Thanh
Camacho Maldonado, Jonathan
Ho, Pei-Shu
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Use 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.
Citation
Thieu, 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.