MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System

MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System Guo-Qiang Zhang1,2 , PhD, Licong Cui1 , PhD, Samden Lhatoo3 , MD, Stephan U. Schuele4 , MD, Satya S. Sahoo2 , PhD 1 Department of EECS, Case Western Reserve University, Cleveland, OH 2 Division of Medical Informatics, Case Western Reserve University, Cleveland, OH 3 Department of Neurology, Case Western Reserve University, Cleveland, OH 4 Department of Neurology, Northwestern Memorial Hospital, Chicago, IL 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. Introduction Epilepsy is the most common serious neurological disorder, affecting 65 million persons worldwide; 150,000 new cases of epilepsy are diagnosed in the United States each year [1]. A third of epilepsy patients fail medical treatment and continue to have seizures [2, 3]. 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 [4]. SUDEP is characterized as “sudden, unexpected, witnessed or unwitnessed, non-traumatic and non-drowning death in an individual with epilepsy, with or without evidence for a seizure and excluding documented status epilepticus where postmortem examination does not reveal a cause for death” [5, 6]. Despite an increasing focus on SUDEP research and its inclusion as an NINDS Area III Epilepsy Research Benchmark priority [4, 7], limited progress has been made in characterizing SUDEP risk factors and mechanisms that lead to death. Effective prevention or treatment approaches are unavailable at present [8, 9]. The 2010 Institute of Medicine (IOM) report, “Elements of an Integrated National Strategy to Accelerate Research and Product Development for Rare Diseases,” recommends a national strategy that “shares research resources and infrastructure to make good and efficient use of scarce funding, expertise, data, and biological specimens.” This recommendation is especially relevant to SUDEP research due to its low rate of reported incidences. For example, the incidence of SUDEP in community-based studies has varied from 0.09 to 0.35 per 100 person-years [10, 11, 12, 13]. 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. Hence, multiple EMUs need to collaborate, share data for building a larger cohort of potential SUDEP patient using state-of-the-art informatics and data analytics infrastructure. In this paper we present the architecture and initial deployment results of MEDCIS, a Multi-Modality Epilepsy Data Capture and Integration System for data integration across multiple EMUs with both retrospective and prospective patient information. MEDICS offers the following collection of main functionalities, each of which has been tested and validated independently:

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1. A standardized data entry platform for patient information at different points of care [14]; 2. An epilepsy-focused natural language processing (NLP) tool to extract patient information from clinical free text in existing patient records [15, 16]; 3. An integrated signal processing application that will allow clinicians to seamlessly interface between signal data and patient information [17, 18, 19]; and 4. A query environment to identify patient cohorts using data integrated from multiple sources based on a shared ontology [20, 21, 22]. 1

Background

MEDCIS has been developed as a part of the NINDS-funded Prevention and Risk Identification of SUDEP Mortality (PRISM) project. PRISM has been led by Case Western Reserve University (CWRU) and involves participating EMUs at the University of California Los Angeles (UCLA) Ronald Reagan Medical Center, Northwestern Memorial Hospital at Northwestern University, and the National Hospital for Neurology and Neurosurgery at University College London (UCL). As part of the PRISM project, we have made significant progress in epilepsy informatics, specifically in area of scalable computing for electrophysiological data using cloud-computing tools [17, 18], paradigm-changing applications of terminological systems for epilepsy research including data capture and data visualization [19, 21], and ontology-driven federated approach to large-scale data integration across multiple centers [20, 22]. This section provides a brief overview of the components that have been developed as a part of an overall integrated environment. 1.1

Epilepsy and Seizure Ontology (EpSO)

EpSO [22] models the necessary domain concepts to describe epilepsy phenotype data at significant level of detail by following an established four-dimensional classification framework in epilepsy [23]. EpSO covers concepts of seizures, location of seizures, etiology and related medical conditions according to the four-dimensional scheme. In addition, it models EEG patterns and comprehensive drug information (anti-epileptic, neuroleptic, and anti-depressants) by using the U.S. National Library of Medicine RxNorm standard [24]. EpSO concepts are mapped to the NINDS Common Data Elements (CDE), which represents nine categories of terms describing imaging, neurological exam, neuropsychology, seizures, and syndromes. 1.2

The Ontology-driven Patient Information Capture (OPIC) system

OPIC uses EpSO to implement a flexible web-based interface to capture data describing demography, patient history, details of paroxysmal events, medication, results of prior electrophysiological evaluations, and patient diagnosis. OPIC leverages EpSO to automatically generate multi-level drop menus that are populated with only relevant terms based on previous user selection (skip patterns) and branching logic to model combinations of user selections. Results of patient evaluation in form of EEG, ECG, and other image files can be directly uploaded and attached with clinical reports in OPIC. The OPIC forms are primarily composed the of structured data entry widgets that reduce user-generated errors, support automated consistency checking, and ensure data completeness, using EpSO as the reference terminology system. 1.3

Epilepsy Data Extraction and Annotation (EpiDEA)

EpiDEA [15, 16] is an ontology-driven clinical free text processing system that extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) [25] for analyzing epilepsy-specific clinical reports. EpiDEA processes two types of textual content in clinical notes: the semi-structured sections with attribute-values pairs and the unstructured sections with sentence-based text. An EpSO-driven epilepsy named entity recognition module and a negation detection module processes the output of these modules. EpSO is used in EpiDEA to support three functionalities: term disambiguation, term normalization, and query expansion using subsumption reasoning. For evaluation, EpiDEA has been used to create a database of 500 patients retrospectively with high precision and recall.

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1.4

Cloudwave

Electrophysiological signal data, such as EEG, are often used as gold standard in the diagnosis and treatment of epilepsy. However, signal information generated during a patient’s admission in an EMU results in very large size multi-modal datasets that cannot be managed using traditional standalone signal processing applications. This is especially important in case of multi-center collaborative clinical studies that require researchers to share and interact with signal data in real time. To address this challenge, we introduced the Cloudwave platform [19] that features a Web-based intuitive signal analysis interface integrated with a Hadoop-based data processing module implemented on clinical data stored in a “private cloud.” Cloudwave provides real-time rendering of multi-modal signals with “montages” for signal feature characterization of multi-modal patient data. Cloudwave also supports signal processing [17] with several magnitudes of speed increase over traditional computing environment. 1.5

VISual Aggregator and Explorer (VISAGE)

VISAGE (Visual Aggregator and Explorer) is a query interface for clinical research cohort search [26]. It was developed for Physio-MIMI (Multi-Modality, Multi-Resource Environment for Physiological and Clinical Research), a multi-CTSA-site collaborative project. Physio-MIMI provides an ontology-driven framework for a federated approach to data integration. The interface design features of VISAGE include auto-generated slider bar, selection boxes, and built-in charting. VISAGE also includes administrative and query lifecycle management functionalities, such as rolebased access control, auditing, query builder, query manager, and query explorer. VISAGE is an interface framework, which can not only ingest ontologies in the OWL format, but also unify ontology navigation activities with query widget generation [27, 28]. 2

Methods

In this section we describe the MEDCIS architecture that integrates the components described in the Background section together into a robust and comprehensive system. The robustness of MEDCIS rests on the centralized usage of the EpSO as the common reference terminology across all of the components (Fig. 1). The consistent use of EpSO in all MEDCIS components is an important step in achieving the architecture integrity. In the following sections we describe in more detail the method and steps of importing EpSO to VISAGE, and the semi-automated mappings that results in unique and beneficial interface features. 2.1

Importing EpSO

We use the Apache Jena Ontology API for parsing the EpSO OWL file to extract all the classes while preserving the class hierarchy that will be used in the query execution module to support subsumption reasoning. The extracted classes and their hierarchical structure are saved in a comma separated value (CSV) file format and imported into VISAGE using a Ruby language script. The imported classes can then be easily searched and navigated by clinicians, physicians, nurses, trainees, biostatisticians, epidemiologists, oncologists, and investigators from non-clinical disciplines in VISAGE query builder to compose query for cohort identification. 2.2

Data mapping and query

The federated data integration approach in Physio-MIMI requires the use of explicit mappings between the source database and the domain ontology, such as EpSO, for query translation and query execution. For MEDCIS, all the relational database components are mapped to appropriate EpSO ontology concepts manually through consultation between the informaticians and epileptologists to ensure quality of mappings. For example, in the table storing the patients interictal EEG pattern information, the column for the pattern content is mapped to EpSO concept “InterictalPattern,” and the column for the pattern location is mapped to the concepts data type property “hasLocation.” The mapping also supports the use of ontology concepts for query construction in the VISAGE query builder module. We use a federated data integration approach in MEDCIS to allow cross-cohort queries over data from multiple centers. Federated data integration is a flexible alternative approach to traditional centralized data warehouse approach that requires periodic Extract Transform Load (ETL) processes to keep data updated in the data warehouse. The federated approach is ideally suited for multi-center studies, allowing tracking and control of data by individual centers while

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B A C

D

F

E

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 cloudcomputing 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.

allowing collaborators with appropriate credentials to query the data. Using EpSO as the common terminology schema, MEDCIS allows integrated querying of data from multiple EMUs. The data from each EMU is maintained in separate databases and mapped to EpSO. After mapping, MEDCIS provides seamless access for comprehensive comparative studies of SUDEP and near-SUDEP cases vs. cohort survivors with subjects from participating EMUs. The key objectives of the VISAGE query interface is to allow clinicians to compose the queries using terms that they are familiar with, combine the terms in different ways (e.g. AND, OR), and explicitly specify negation of specific conditions (e.g. patient not prescribed Keppra). We have already shown in the Physio-MIMI project that VISAGE has multiple advantages over traditional query composition interfaces that often closely reflected the SQL query structure, which is not an intuitive structure for clinical researchers. MEDCIS instantiates the design template of VISAGE using EpSO, thus achieving an interface specifically for the multi-center SUDEP study. 2.2.1

Query widget composition

Each concept in EpSO can generate a visual “query widget” that reflects the specific type of the concept and includes the sub-classes of the concept. For example, a researcher searching for patients with Aura can just select the concept Aura from the “ontology search and browsing” section (Fig. 3), and the appropriate query widget is automatically generated (Fig. 2). The top part of the query widget is the “Aura” class hierarchy as defined in EpSO, and by default includes all its subclasses. In addition to its subclasses, Fig. 2 also shows the ontology property associated with a given class, for example an Aura is associated with the laterality in a patient. The query widget automatically identifies the appropriate values for the laterality information associated with an Aura and displays on the interface for selection by the user.

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To explore subclasses of a concept such as “AuditoryAura,” a user can click “AuditoryAura” and a new widget is rendered in the query composition interface with specific subclasses of the concept (lower part of Fig. 2). This seamlessly provides the ontological browsing of the concept hierarchy. The user can also easily remove specific query widgets that are no longer needed. This allows users to flexibly modify and update their query structure as they explore the ontology classes and are interested in exploring new hypothesis. 2.2.2

Query generation

Figure 2: Query widget for the concept “Aura,” which automatically generates the display boxes for its sub-

The query widgets are translated into the SQL query statement classes, including “AuditoryAura” (upper). Clicking “Authat is executed over the relational database storing the patient ditoryAura” automatically generates the display boxes for data. For each query widget, the EpSO instantiated VISAGE its subclasses as well (lower). interface records the identifier concept, query concept, data type property of the query concept as well as their selections. It relies on the “database to ontology” mapping to generate the appropriate query. The VISAGE query module does not require each ontology concept to be mapped to a database component. Instead, it leverages the ontology class structure to search for the closest mapped ancestor of the concept that forms the query widget to generate the relational database query. For example, the concept “Aura” is not mapped to any of the data source column, although VISAGE automatically find its closest ancestor “Seizure” which is mapped to the data source column capturing the seizure semiology information. In the next step, the query generation module identifies the relational database column labeled as “seizure semiology” found in the table “seizure semiologies.” In addition to the class, the original de-identified discharge summary for the selected patient is also retrieved using the DocumentID column in the “seizure semiologies” table. The property of the class, such as “Laterality” of “AuditoryAura,” is mapped to the column laterality in the table “seizure semiologies.” The following sample SQL query statement is created by the query generation module for a query to identify a patient cohort with “Auditory Aura” and having “Right” laterality: SELECT count(DISTINCT seizure_semiologies.‘doc‘) FROM seizure_semiologies WHERE (CAST(seizure_semiologies.‘seizure_semiology‘ AS CHAR(255)) IN (’AuditoryAura’, ’ComplexAudi-toryAura’, ’ElementaryAuditoryAura) and CAST(seizure_semiologies.‘laterality‘ AS CHAR(255)) IN (’Right’)) An important feature of the query generation module is the ability to support subsumption reasoning based on the EpSO class hierarchy. For example, selecting “Aura” not only includes the eight subtypes as indicated in Fig. 3, but also includes all their descendants. Multiple query widgets can be combined by AND or OR, depending on how the user groups the widgets (Fig. 3). 3

Results

Our prototype implementation involved patients from University Hospitals Case Medical Center (UH CMC) EMU and the EMU at Northwestern Memorial Hospital (NMH) of Northwestern University (under an appropriate IRBs and Data Use Agreement). Multi-modal data of a total of 936 epilepsy patients are currently in MEDCIS. Of the 936 patients, 504 were processed through EpiDEA retrospectively and 432 were captured prospectively using OPIC. All patients processed through EpiDEA were from UH CMC EMU, and 57 captured by OPIC were from NMH. Patients from UH CMC EMU and NMH were stored in different databases and then federated through MEDCIS using EpSO and our mapping module. For the 504 patients EpiDEA processed a total of 100,836 sentences, 603,605 word tokens and 250,387 noun phrases. 212,246 noun phrases were mapped to appropriate EpSO classes. EpiDEA achieved an overall precision of 93.59%, a recall of 84.01% and an F-measure of 88.53%, as reported in [15]. The MEDCIS database schema consisted of seven tables capturing information about etiology, epileptogenic zone, seizure semiology, lateralizing sign, interictal EEG pattern, ictal EEG pattern, and medication, in addition to demographics.

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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.

Multi-modal signal data of a subset of the patients from the UH CMC EMU were linked to Cloudwave through the VISAGE interface. More than 77GB of signal data were processed using the Cloudwave pipeline and made available for rendering through the web-interface. 3.1

Query result rendering

Query results are rendered in format as shown in Fig. 4, listing each patient’s gender, age, epileptogenic zone, and EEG pattern as well as location that are clinically relevant. 3.2

Linking to multi-modal data

VISAGE provided a hyperlink (the first column of the table in Fig. 4) to the original discharge summary report and electrophysiological signal data (Fig. 5) of the patient that can be reviewed further by the clinical researcher. 3.3

Evaluation

Each of the MEDCIS components already have their respective evaluations performed and reported as published results [14, 15, 17]. The basic functionality of the VISAGE query environment was evaluated in terms of the usability by clinical investigators as part of the Phsyio-MIMI project [26]. For MEDCIS, our evaluation focused on two integrative aspects. One is expressiveness (Section 3.3.1): the ability

Figure 4: Sample screenshot of the query result. The result is displayed in a tabular format with data for key fields shown.

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Figure 5: Sample screenshot of the links from query results to discharge summary reports and Cloudwave viewer.

of the query interface to support the type of questions a clinical investigator would like to ask. The other is validity (Section 3.3.2): for those questions that can be translated into appropriate queries, the degree of agreement that the data reported in the VISAGE resulting reports with the information contained in the original patient discharge summaries. 3.3.1

Expressiveness of the EpSO-driven query interface

We focused on the utility of the EpSO domain-specific ontology in supporting the intuitive construction of queries of clinical interest in the PRISM project. 40 questions of clinical interest were formulated by clinical investigators in the UH CMC EMU. Three such questions that were captured using VISAGE are displayed below. Figure 6 (top of next page) shows the screen capture of the query corresponding to the first question. • Find all patients ages between 20-65 with generalized tonic clonic seizure exhibiting lateralizing sign. • Find all female patients who have taken Depakote in the past, are currently on Keppra, with either abdominal aura or autonomic aura. • Find all patients with right visual aura and slow spike EEG pattern from left occipital lobe. Of the 40 questions, about 26% did not have a direct translation to a query in VISAGE. The non-translatable questions can be classified into three types. The first type of questions unable to be captured in VISAGE is due to complexity. For example, the question “Find all patients who had been on only two anti-epileptic medications” did not have a simple translation. There are 144 distinct epileptic related drugs. Take a combination of exactly two from this list will result in 10296 choices. This clearly requires a program to execute, and is not an issue that can be easily addressed by a query interface such as VISAGE directly, as far as we know. The second type of questions unable to be captured in VISAGE is due to limited information capture in the data source. For example, the query “All patients with history of illegal drug use, now or in the past (psychosocial history) and having epileptic paroxysmal episodes (diagnosis)” is not supported because no data on illegal drug use were captured in the EMU. The third type of questions unable to be captured in VISAGE is due to the incompleteness of the EpSO terminology system. For example, we were not able to capture “All patients with diagnosis of epileptic paroxysmal episodes and periodic limb movement disorder/nocturnal myoclonus” because EpSO has not covered diseases such as “limb movement disorder.” This points to the need to continue expanding and evolving EpSO concurrently with the need and scientific advances in the field. In summary, of the 17 questions not captured by VISAGE, 9 were due to complexity of translation, 5 due to limited source information, and 3 due to terminology incompleteness.

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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.”

3.3.2

Validity of query results against the ground truth

For the 74% of the remaining questions captured using VISAGE queries, we manually validated their correctness by using the linked discharge summery reports as primary source, and inspected the results to be correct. However, since EpiDEA, as any other NLP tool, cannot possibly reach a 100% precision and a 100% recall, its actual precision of 93.59% and recall of 84.01% will impact the query results compared to ground truth (i.e., information found in discharge summaries). We expect OPIC captured patient information to achieve near 100% precision and near 100% recall, barring human data entry errors (although we have not independently evaluated this aspect of data entry quality). 4

Discussions

The functionality of MEDCIS, in terms of query composition, is only limited by the number of concepts modeled in EpSO. At present EpSO has not covered all existing epilepsy subspecialties, such as pediatric epilepsy. As the PRISM project continues, we are engaging the epilepsy community to expand and enhance the concepts modeled in EpSO to address this limitation. We also propose to conduct a comprehensive user survey, spanning six months, with the new batch of medical residents in the UH CMC EMU for further analysis of MEDCIS features. This will help us to maintain and update MEDCIS with new features according to changing user requirements in the PRISM project. A larger EMU consortium has been formed, involving additional EMUs. Multi-modal physiological, biochemical, phenotypic, genetic and imaging data are to be collected for a targeted number of 2500 epilepsy patients for the next five years. MEDCIS has been adopted as the informatics and data infrastructure hosting this unique and largest nationally shared SUDEP research resource. The reported SUDEP incidence in adult EMUs is about 5 per 1000 per year [29]. Since SUDEP is a rare event, we had no SUDEP incidence at UH CMC EMU while the patients were under active mornitoring during the PRISM project. Two SUDEP deaths among the UH CMC EMU patients occurred outside the EMU. In the next five years, we expect 10 to 20 SUDEP cases in UH CMC EMU, accounting for patient population growth. Prospectively capturing

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all available EMU patient information, and sharing of such information from multiple EMUs, is critical for advancing the understanding the mechanism of SUDEP. 5

Conclusion

This paper presented key architecture and interface features of the MEDCIS platform to address the challenges of integrating structured and semi-structured information from multiple EMUs for SUDEP research. Our experience shows that the ontology-driven architecture for MEDCIS coupling the EpiDEA clinical free text processing tool, the OPIC structured data capturing interface, and the VISAGE query interface, provides a scalable solution to the data extraction and querying requirements in multi-center clinical research projects exemplified by the PRISM study. Acknowledgement. This research was supported by the PRISM (Prevention and Risk Identification of SUDEP Mortality) Project (1-P20-NS076965-01) and in part by the Case Western Reserve University CTSA Grant NIH/NCATS UL1TR000439. References [1] Epilepsy Foundation. Available from: http://www.epilepsyfoundation.org/aboutepilepsy/whatisepilepsy/statistics.cfm. Accessed August 2nd, 2014. [2] Boon P, Vonck K, De Herdt V, Van Dycke A, Goethals M, Goossens L, Van Zandijcke M, De Smedt T, Dewaele I, Achten R, Wadman W, Dewaele F, Caemaert J, Van Roost D. Deep brain stimulation in patients with refractory temporal lobe epilepsy. Epilepsia. 2007;48(8):1551-60. [3] Fisher RS. Emerging antiepileptic drugs. Neurology. 1993;43(suppl):12-20. [4] Tomson T, Nashef L, Ryvlin P. Sudden unexpected death in epilepsy: current knowledge and future directions. Lancet neurology. 2008;7(11):1021-31. [5] Nashef L, So EL, Ryvlin P, Tomson T. Unifying the definitions of sudden unexpected death in epilepsy. Epilepsia. 2012;53(2):227-33. [6] Nashef L. Sudden unexpected death in epilepsy: terminology and definitions. Epilepsia. 1997;38(Suppl 11):S6-8. [7] Kelley MS, Jacobs MP, Lowenstein DH. The NINDS epilepsy research benchmarks. Epilepsia. 2009;50(3):57982. [8] Tomson T, Walczak T, Sillanpaa M, Sander JW. Sudden unexpected death in epilepsy: a review of incidence and risk factors. Epilepsia. 2005;46 (Suppl 11):54-61. [9] So EL. What is known about the mechanisms underlying SUDEP? Epilepsia. 2008;49 (Suppl 9):93-8. [10] Tomson T, Nashef L, Ryvlin P. Sudden unexpected death in epilepsy: current knowledge and future directions. Lancet neurology. 2008;7(11):1021-31. Epub 2008/09/23. [11] Tomson T, Walczak T, Sillanpaa M, Sander JW. Sudden unexpected death in epilepsy: a review of incidence and risk factors. Epilepsia. 2005;46 (Suppl 11):54-61. [12] Lhatoo SD, Sander JWAS. The epidemiology of epilepsy and learning disability. Epilepsia. 2001;42(s1):6-9. [13] Ficker DM, So EL, Shen WK, Annegers JF, O’Brien PC, Cascino GD, Belau PG. Population-based study of the incidence of sudden unexplained death in epilepsy. Neurology. 1998;51(5):1270-4. [14] Sahoo SS, Zhao M, Luo L, Bozorgi A, Gupta A, Lhatoo SD, Zhang GQ. OPIC: Ontology-driven patient information capturing system for epilepsy. The American Medical Informatics Association (AMIA) Annual Symposium, 2012;2012:799-808. [15] Cui L, Bozorgi A, Lhatoo SD, Zhang GQ, Sahoo SS. EpiDEA: Extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. The American Medical Informatics Association (AMIA) Annual Symposium, 2012;2012:1191-1200.

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[16] Cui L, Sahoo SS, Lhatoo SD, Garg G, Rai P, Bozorgi A, Zhang GQ. Complex epilepsy phenotype extraction from narrative clinical discharge summaries. Journal of Biomedical Informatics, Published Online: June 25, 2014, doi: http://dx.doi.org/10.1016/j.jbi.2014.06.006. [17] Sahoo S, Jayapandian C, Garg G, Kaffashi F, Chung S, Bozorgi A, Chen CH, Loparo K, Lhatoo SD, and Zhang GQ. Heart beats in the cloud: distributed analysis of electrophysiological “big data” using cloud computing for epilepsy clinical research. J Am Med Inform Assoc. 2014 Mar 1;21(2):263-71. [18] Jayapandian CP, Chen CH, Bozorgi A, Lhatoo SD, Zhang GQ, Sahoo SS. Cloudwave: distributed processing of “Big Data” from electrophysiological recordings for epilepsy clinical research using Hadoop. The American Medical Informatics Association (AMIA) Annual Symposium, 2013 Nov 16;2013:691-700. [19] Jayapandian CP, Chen CH, Bozorgi A, Lhatoo SD, Zhang GQ, Sahoo SS. Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research. Stud Health Technol Inform. 2013;192:817-21. [20] Zhang GQ, Sahoo SS, Lhatoo SD. From classification to epilepsy ontology and informatics. Epilepsia 2012: 53 (2 Suppl): 28-32. [21] Sahoo SS, Zhang GQ, Lhatoo SD. Epilepsy informatics and an ontology-driven infrastructure for large database research and patient care in epilepsy. Epilepsia. 2013;54(8):1335-41. [22] Sahoo SS, Lhatoo SD, Gupta DK, Cui L, Zhao M, Jayapandian C, Bozorgi A, Zhang GQ. Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care. Journal of American Medical Association. 2014;21(1):82-9. [23] L¨uders HO, Amina, S., et al. Modern technology calls for a modern approach to classification of epileptic seizures and the epilepsies. Epilepsia. 2012;53(3):405-11. [24] Bodenreider O, Peters L, Nguyen T. RxNav: Browser and application programming interfaces for drug information sources. AMIA Annual Symposium, 2011:2129 (demo). [25] Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17(5):507-513. [26] Zhang GQ, Siegler T, Saxman P, Sandberg N, Mueller R, Johnson N, Hunscher D, and Arabandi S. VISAGE: a query interface for clinical research. AMIA Jt Summits Transl Sci Proc. 2010 Mar 1;2010:76-80. [27] Zhang GQ, Cui L, Teagno J, Kaebler D, Koroukian S, Xu R. Merging ontology navigation with query construction for web-based medicare data exploration. AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:285-9. [28] Cui L, Mueller R, Sahoo SS, Zhang GQ. Querying complex federated clinical data using ontological mapping and subsumption reasoning. IEEE International Conference on Healthcare Informatics 2013 (ICHI 2013), pp. 351-360. [29] Ryvlin P, Nashef L, Lhatoo SD, Bateman LM, Bird J, Bleasel A, et al. Incidence and mechanisms of cardiorespiratory arrests in epilepsy monitoring units (MORTEMUS): a retrospective study. The Lancet Neurology, 2013;12(10):966-977.

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