Project Leaders

1. Joseph Picone, PhD; Iyad Obeid, PhD
Neural Engineering Data Consortium
College of Engineering, Temple University
Philadelphia, Pennsylvania, U.S.A.

2. Sanda M. Harabagiu, PhD
Human Language Technology Research Institute
University of Texas at Dallas
Dallas, Texas, U.S.A.

Electronic medical records (EMRs) collected at every hospital in the country collectively contain a staggering wealth of biomedical knowledge. EMRs can include unstructured text, temporally constrained measurements (e.g., vital signs), multichannel signal data (e.g., EEGs), and image data (e.g., MRIs). This information could be transformative if properly harnessed. Information about patient medical problems, treatments, and clinical course is essential for conducting comparative effectiveness research. Uncovering clinical knowledge that enables comparative research is the primary goal of this research.

Our focus in this research project is the automatic interpretation of a clinical EEG big data resource known as the TUH EEG Corpus (TUH EEG). This corpus was collected over 14 years at Temple University Hospital and consists of over 28,000 sessions and 15,000 patients. Clinicians will be able to retrieve relevant EEG signals and EEG reports using standard queries (e.g. “Young patients with focal cerebral dysfunction who were treated with Topamax”). We will automatically annotate EEG events that contribute to a diagnosis. Automated techniques are used to discover and time-align the underlying EEG events using semi-supervised learning. Clinical concepts, their type, polarity and modality are being discovered automatically, as well as spatial and temporal information. In addition, we are extracting the medical concepts describing the clinical picture of patients from the EEG reports. We are developing a patient cohort retrieval system that will operate on the extracted clinical knowledge.

An important outcome of this research will be the existence of an annotated big data archive of EEGs that will greatly increase accessibility for non-experts in neuroscience, bioengineering and medical informatics who would like to study EEG data. The creation of this resource through the development of efficient automated data wrangling techniques will demonstrate that a much wider range of big data bioengineering applications are now tractable.