Processing free-text input to obtain a database of medical information
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Tagging English text with a probabilistic model
Computational Linguistics
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
DILEMMA-2: a lemmatizer-tagger for medical abstracts
ANLC '92 Proceedings of the third conference on Applied natural language processing
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We propose a semantic tagger that provides high level concept information for phrases in clinical documents. It delineates such information from the statements written by doctors in patient records. The tagging, based on Hidden Markov Model (HMM), is performed on the documents that have been tagged with Unified Medical Language System (UMLS), Part-of-Speech (POS), and abbreviation tags. The result can be used to extract clinical knowledge that can support decision making or quality assurance of medical treatment.