A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling. (March 2021) - Thieu, Thanh; Maldonado, Jonathan Camacho; Ho, Pei-Shu; Ding, Min; Marr, Alex; Brandt, Diane; Newman-Griffis, Denis; Zirikly, Ayah; Chan, Leighton; Rasch, Elizabeth
Graphical abstract: Highlights: Functioning terminology is underpopulated in electronic health records and underrepresented in the Unified Medical Language System. This is a comprehensive analysis of the Mobility domain of the ICF, including entity analysis, annotation, and machine sequence labeling. Low-resourced and nested Mobility concepts can be accurately identified by using transfer learning re-trained on an adequate corpus. Abstract: Background: Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked in accessing patients' health status. The World Health Organization (WHO)'s International Classification of Functioning, Disability and Health (ICF) is considered the international standard for describing and coding function and health states. We pioneer the first comprehensive analysis and identification of functioning concepts in the Mobility domain of the ICF. Results: Using physical therapy notes at the National Institutes of Health's Clinical Center, we induced a hierarchical order of mobility-related entities including 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 high-performance Ensemble machine learning model for named entity recognition (NER) was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our Ensemble method (84.90 %) outperformed popular NER methods: Conditional Random Field (80.4 %), Recurrent Neural Network (81.82 %), and Bidirectional Encoder Representations from Transformers (82.33 %). Conclusions: The results of this study show that mobility functioning information can be reliably captured from clinical notes once adequate resources are provided for sequence labeling methods. We expect that functioning concepts in other domains of the ICF can be identified in similar fashion.