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
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Healthcom'09 Proceedings of the 11th international conference on e-Health networking, applications and services
Semantic techniques for the web
High accuracy information retrieval and information extraction system for electronic clinical notes
HIKM '10 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 108
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Automatic identification of biomedical concepts in spanish-language unstructured clinical texts
Proceedings of the 1st ACM International Health Informatics Symposium
Exploiting semantic structure for mapping user-specified form terms to SNOMED CT concepts
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
An ontology for clinical questions about the contents of patient notes
Journal of Biomedical Informatics
Ontologies and terminologies: Continuum or dichotomy?
Applied Ontology - Ontologies and Terminologies: Continuum or Dichotomy?
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The automatic conversion of free text into a medical ontology can allow computational access to important information currently locked within clinical notes and patient reports. This system introduces a new method for automatically identifying medical concepts from the SNOMED Clinical Terminology in free text in near real time. The system presented consists of 3 modules; an Augmented Lexicon, term compositor and negation detector. The Augmented Lexicon indexes the SNOMED-CT terms, the term compositor finds qualification relationships between concepts and the negation detector identifies negative concepts. The system delivers the services through a variety of interfaces including direct programming access and web-based access. It is currently in use in a hospital environment to capture patient data response with SNOMED-CT codes in real time at the point of care. No strict evaluation has been done on the system to date, however preliminary results indicate performance within acceptable time and accuracy limits.