Work

Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification.

Year: 2017

Type: article

Abstract: The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework which combines the advantages of active and deep learning while producing annotations that capture a variety of attributes of medical concepts. Results obtained through our novel framework show great promise.

Source: PubMed Central

Authors: Ramon Maldonado, Travis R Goodwin, Sanda M Harabagiu

Institution: The University of Texas at Dallas

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Human Genome Research Institute

Award: Automatic discovery and processing of EEG cohorts from clinical records