Lead Inventors: Stephen Bennett Johnson, David Campbell, Eneida Mendonca, Robert Duffy, Chunhua Weng.
Biomedical Software to convert Free-Form Prose from Medical Reports are Costly and Time Consuming to DevelopAcQUisitive Analyzer for Biomedical Text Uses Machine Learning Algorithm
AQUA, the AcQUisitive Analyzer for Biomedical Text, uses a machine learning algorithm, transformation based learning, that automatically generates parsing rules based on sample ""training sets,"" specific to a type of biomedical input text. A scoring function is iteratively applied to select and build the set of parsing rules based on how accurately each rule transforms and parses the training set text. The training sets are manually parsed based on syntax alone - a job that requires only grammatical knowledge, not medical or linguistic expertise. Each training set is specific to a domain of medical text, and the AQUA algorithm can be applied to any type of input with a sufficient training set.
Advantages:
• Can be applied to virtually any type of biomedical input text without changing the core software, simply by using a training set of sentences from the domain of desired input.
• No linguistic expertise needed to prepare the test set of sentences used to train the algorithm to take new forms of input.
Licensing Status: Available for Licensing
Publications: David Campbell, Stephen Johnson. A transformational-based learner for dependency grammars in discharge summaries. Proceedings of the Association for Computational Linguistics Workshop on Natural Language Processing in the Biomedical Domain, 37-44. Philadelphia, July 2002.