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. 2014 Nov 14;2014:1248-57.
eCollection 2014.

MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System

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Free PMC article

MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System

Guo-Qiang Zhang et al. AMIA Annu Symp Proc. .
Free PMC article

Abstract

Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death and is most common in patients with intractable, frequent, and continuing seizures. A statistically significant cohort of patients for SUDEP study requires meticulous, prospective follow up of a large population that is at an elevated risk, best represented by the Epilepsy Monitoring Unit (EMU) patient population. Multiple EMUs need to collaborate, share data for building a larger cohort of potential SUDEP patient using a state-of-the-art informatics infrastructure. To address the challenges of data integration and data access from multiple EMUs, we developed the Multi-Modality Epilepsy Data Capture and Integration System (MEDCIS) that combines retrospective clinical free text processing using NLP, prospective structured data capture using an ontology-driven interface, interfaces for cohort search and signal visualization, all in a single integrated environment. A dedicated Epilepsy and Seizure Ontology (EpSO) has been used to streamline the user interfaces, enhance its usability, and enable mappings across distributed databases so that federated queries can be executed. MEDCIS contained 936 patient data sets from the EMUs of University Hospitals Case Medical Center (UH CMC) in Cleveland and Northwestern Memorial Hospital (NMH) in Chicago. Patients from UH CMC and NMH were stored in different databases and then federated through MEDCIS using EpSO and our mapping module. More than 77GB of multi-modal signal data were processed using the Cloudwave pipeline and made available for rendering through the web-interface. About 74% of the 40 open clinical questions of interest were answerable accurately using the EpSO-driven VISual AGregagator and Explorer (VISAGE) interface. Questions not directly answerable were either due to their inherent computational complexity, the unavailability of primary information, or the scope of concept that has been formulated in the existing EpSO terminology system.

Figures

Figure 1:
Figure 1:
Architecture and data workflow of MEDCIS. A. EpiDEA is used for retrospective information extraction from clinical free-text; B. OPIC is used for prospective structured data capture; C. EpSO is used for mostly other components behind the scenes, but is most directly visible in the VISAGE query interface, which incorporates a built-in ontology browser; D. Once data from EpiDEA and OPIC are ingested into a common database, possibly from multiple sources, the VISAGE interface can be directly accessed by investigators to perform cohort search; E. Multi-modal signal data is processed and annotated using distributed a cloud-computing approach; F. The signal data can be visualized based on the cohort returned from VISAGE, which provides a direct link to the signal data and other clinical documents.
Figure 2:
Figure 2:
Query widget for the concept “Aura,” which automatically generates the display boxes for its subclasses, including “AuditoryAura” (upper). Clicking “AuditoryAura” automatically generates the display boxes for its subclasses as well (lower).
Figure 3:
Figure 3:
Left: Screenshot of the VISAGE cohort search interface guided by the EpSO ontology. Data from multiple sites are mapped to EpSO, allowing VISAGE to query across projects. The query interface is “driven” by EpSO in that the available seizure types and locations are automatically generated as check boxes for user selection. Right: Screenshot of the Cloudwave web-based signal visualization interface featuring montage composition, events overlay, and a dashboard displaying positioning information which allows the signal to be rendered at multiple desirable resolution.
Figure 4:
Figure 4:
Sample screenshot of the query result. The result is displayed in a tabular format with data for key fields shown.
Figure 5:
Figure 5:
Sample screenshot of the links from query results to discharge summary reports and Cloudwave viewer.
Figure 6:
Figure 6:
Screenshot of VISAGE query interface corresponding to the criteria “all patients ages between 20–65 with generalized tonic clonic seizure exhibiting lateralizing sign.”

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