BioNLP 2013
نویسنده
چکیده
This research analyzed the clinical notes of epilepsy patients using techniques from corpus linguistics and machine learning and predicted which patients are candidates for neurosurgery, i.e. have intractable epilepsy, and which are not. Information-theoretic and machine learning techniques are used to determine whether and how sets of clinic notes from patients with intractable and nonintractable epilepsy are different. The results show that it is possible to predict from an early stage of treatment which patients will fall into one of these two categories based only on text data. These results have broad implications for developing clinical decision support systems.
منابع مشابه
Overview of BioNLP Shared Task 2013
The BioNLP Shared Task 2013 is the third edition of the BioNLP Shared Task series that is a community-wide effort to address fine-grained, structural information extraction from biomedical literature. The BioNLP Shared Task 2013 was held from January to April 2013. Six main tasks were proposed. 38 final submissions were received, from 22 teams. The results show advances in the state of the art ...
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