ONYX: a system for the semantic analysis of clinical text
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
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
A neuro-oncology workstation for structuring, modeling, and visualizing patient records
Proceedings of the 1st ACM International Health Informatics Symposium
Journal of Biomedical Informatics
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A parser for medical free text reports has been developed that is based on a chemistry/physics inspired ldquofield theoryrdquo for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of ldquoworkrdquo required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9% and 89.9%, respectively.