A key prerequisite for high-quality healthcare delivery is effective communication within and across healthcare settings. However, communication can be hampered by the pervasive use of abbreviations in clinical notes. Clinicians use abbreviations to save time during documentation. While abbreviations may seem unambiguous to their authors, they often cause confusion to other readers, including healthcare providers, patients, and natural language processing (NLP) systems attempting to extract clinical terms from text. While the understanding that abbreviations can cause errors is widespread, few have deployed pragmatic solutions for this important problem. The proposed project will develop, evaluate, and share a systematic approach to Clinical Abbreviation Recognition and Disambiguation (CARD), and in doing so substantially aims to benefit existing NLP systems and to improve computer-based documentation systems by reducing ambiguities in electronic records in real-time. The study includes the following five Specific Aims: 1) Develop automated methods to detect abbreviations and their senses from clinical text corpora and build a comprehensive knowledge base of clinical abbreviations;2) Develop and evaluate three automated word sense disambiguation (WSD) classifiers, and establish methods to combine those classifiers to maximize both their performance and coverage;3) Develop the CARD system, and demonstrate its effectiveness by integrating it with two established NLP systems (MedLEE and KnowledgeMap);4) Integrate CARD with an institutional clinical documentation system (Vanderbilt's StarNotes) and evaluate its ability to expand abbreviations in real-time as clinicians generate records;5) Distribute the CARD knowledge base and software for non-commercial uses.
Abbreviations are widely used throughout all types of clinical documents and they cause confusion to both healthcare providers and patients and limit effective communications within and across care settings. This proposed study will develop informatics methods to automatically detect abbreviations and their possible meanings from large clinical text and to disambiguate abbreviations that have multiple meanings. We will also integrate those methods with clinical documentation systems so that abbreviations will be expanded in real-time when physicians entering clinical notes, thus to improve the quality of health records.
|Wu, Yonghui; Lei, Jianbo; Wei, Wei-Qi et al. (2013) Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages. Stud Health Technol Inform 192:662-6|
|Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu et al. (2013) Applying active learning to supervised word sense disambiguation in MEDLINE. J Am Med Inform Assoc 20:1001-6|
|Chen, Yukun; Mani, Subramani; Xu, Hua (2012) Applying active learning to assertion classification of concepts in clinical text. J Biomed Inform 45:265-72|
|Wu, Yonghui; Rosenbloom, S Trent; Denny, Joshua C et al. (2011) Detecting abbreviations in discharge summaries using machine learning methods. AMIA Annu Symp Proc 2011:1541-9|