IIIT Hyderabad Publications |
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A hybrid deep learning approach for medical relation extractionAuthors: raghavendra.ch ,Kamalakar Karlapalem Conference: 24TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD-2015 2018) Location London Date: 2018-08-19 Report no: IIIT/TR/2018/74 AbstractMining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task. Full paper: pdf Centre for Data Engineering |
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