Designing a controlled medical vocabulary server: the VOSER project
Computers and Biomedical Research
Mastering regular expressions
Journal of the American Society for Information Science
Towards linking patients and clinical information: detecting UMLS concepts in e-mail
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Exploring two biomedical text genres for disease recognition
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Detecting Intuitive Mentions of Diseases in Narrative Clinical Text
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Building a semantically annotated corpus of clinical texts
Journal of Biomedical Informatics
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Tracking medical students' clinical experiences using natural language processing
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Selecting information in electronic health records for knowledge acquisition
Journal of Biomedical Informatics
Section classification in clinical notes using supervised hidden markov model
Proceedings of the 1st ACM International Health Informatics Symposium
Journal of Biomedical Informatics
Compositional information extraction methodology from medical reports
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Semantic mappings and locality of nursing diagnostic concepts in UMLS
Journal of Biomedical Informatics
Enhancing clinical concept extraction with distributional semantics
Journal of Biomedical Informatics
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In this study, we evaluate the performance of a Natural Language Processing (NLP) application designed to extract medical problems from narrative text clinical documents. The documents come from a patient's electronic medical record and medical problems are proposed for inclusion in the patient's electronic problem list. This application has been developed to help maintain the problem list and make it more accurate, complete, and up-to-date. The NLP part of this system-analyzed in this study-uses the UMLS MetaMap Transfer (MMTx) application and a negation detection algorithm called NegEx to extract 80 different medical problems selected for their frequency of use in our institution. When using MMTx with its default data set, we measured a recall of 0.74 and a precision of 0.756. A custom data subset for MMTx was created, making it faster and significantly improving the recall to 0.896 with a non-significant reduction in precision.