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Abstract: The automatic identification of relations between medical concepts in a large corpus of Electroencephalography (EEG) reports is an important step in the development of an EEG-specific patient cohort retrieval system as well as in the acquisition of EEG-specific knowledge from this corpus. EEG-specific relations involve medical concepts that are not typically mentioned in the same sentence or even the same section of a report, thus requiring extraction techniques that can handle such long-distance dependencies. To address this challenge, we present a novel framework which combines the advantages of a deep learning framework employing Dynamic Relational Memory (DRM) with active learning. While DRM enables the prediction of long-distance relations, active learning provides a mechanism for accurately identifying relations with minimal training data, obtaining an 5-fold cross validation F1 score of 0.7475 on a set of 140 EEG reports selected with active learning. The results obtained with our novel framework show great promise.

Learning Objective 1: Apply deep neural learning and active learning to detect relations between distant medical concepts

Authors:

Ramon Maldonado (Presenter)
The University of Texas at Dallas

Travis Goodwin, The University of Texas at Dallas
Sanda Harabagiu, The University of Texas at Dallas

Presentation Materials:

Keywords