![]() ![]() ![]() These familiar linguistic structures provide a context for information, and context is a critical factor in differentiating whether “discharge” is from a nose, and therefore a symptom, or from a hospital, in which case it signifies an end to a patient encounter. 2Ĭuriously, the aspects of language that are taught at the earliest age-how to recognize a sentence, a section, a paragraph-can be the most difficult from an NLP perspective. 1 Today, the majority of critical patient information is unstructured in narrative notes, so CAC through NLP holds great promise, and it is in this area where the clinical document architecture (CDA) and the project known as CDA4CDT (clinical document architecture for common document types) have a role to play. How CDA4CDT Supports CACĬAC uses either structured input or natural language processing (NLP). This paper describes the clinical document architecture (CDA) and the CDA4CDT (clinical document architecture for common document types) initiative and suggests how adoption of these standards supports computer-assisted coding (CAC). ![]()
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