Hierarchical semantic structures for medical NLP.
We present a framework for building a medical natural language processing (NLP) system capable of deep understanding of clinical text reports. The framework helps developers understand how various NLP-related efforts and knowledge sources can be integrated. The aspects considered include: 1) computational issues dealing with defining layers of intermediate semantic structures to reduce the dimensionality of the NLP problem; 2) algorithmic issues in which we survey the NLP literature and discuss state-of-the-art procedures used to map between various levels of the hierarchy; and 3) implementation issues to software developers with available resources. The objective of this poster is to educate readers to the various levels of semantic representation (e.g., word level concepts, ontological concepts, logical relations, logical frames, discourse structures, etc.). The poster presents an architecture for which diverse efforts and resources in medical NLP can be integrated in a principled way.
Department of Radiological Sciences, University of California, Los Angeles, USA.
Ricky K
RK
Taira
Corey W
CW
Arnold
D000465:Algorithms; D004282:Documentation / Q000379:methods; D057286:Electronic Health Records / Q000145:classification* / Q000458:organization & administration*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics; D018875:Vocabulary, Controlled