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Xia Y, Duan Y, Sha L, Lai W, Zhang Z, Hou J, Chen L. Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application. BMC Med Inform Decis Mak 2024; 24:101. [PMID: 38637746 PMCID: PMC11027401 DOI: 10.1186/s12911-024-02509-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. MATERIALS AND METHODS The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. RESULTS WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. CONCLUSIONS Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.
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Affiliation(s)
- Yilin Xia
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Yifei Duan
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Leihao Sha
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Wanlin Lai
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Zhimeng Zhang
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Jiaxin Hou
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China
| | - Lei Chen
- Department of Neurology, West China Hospital, Sichuan University, #37 Guoxue Alley, Wuhou District, 610041, Chengdu, Sichuan Province, China.
- Pazhou Lab, Guangzhou, China.
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Sivagnanam S, Yeu S, Lin K, Sakai S, Garzon F, Yoshimoto K, Prantzalos K, Upadhyaya DP, Majumdar A, Sahoo SS, Lytton WW. Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain. Database (Oxford) 2024; 2024:baae023. [PMID: 38581360 PMCID: PMC10998337 DOI: 10.1093/database/baae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/22/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC-NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org.
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Affiliation(s)
- S Sivagnanam
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Biomedical Engineering, SUNY Downstate Health Sciences University, 450 Clarkson Avenue, Brooklyn, NY 11203, USA
| | - S Yeu
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - K Lin
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - S Sakai
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - F Garzon
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - K Yoshimoto
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - K Prantzalos
- School of Medicine, Case Western University, 9501 Euclid Ave, Cleveland, OH 44106, USA
| | - D P Upadhyaya
- School of Medicine, Case Western University, 9501 Euclid Ave, Cleveland, OH 44106, USA
| | - A Majumdar
- San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - S S Sahoo
- School of Medicine, Case Western University, 9501 Euclid Ave, Cleveland, OH 44106, USA
| | - W W Lytton
- Biomedical Engineering, SUNY Downstate Health Sciences University, 450 Clarkson Avenue, Brooklyn, NY 11203, USA
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3
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Issabekov G, Matsumoto T, Hoshi H, Fukasawa K, Ichikawa S, Shigihara Y. Resting-state brain activity distinguishes patients with generalised epilepsy from others. Seizure 2024; 115:50-58. [PMID: 38183828 DOI: 10.1016/j.seizure.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/14/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024] Open
Abstract
PURPOSE Epilepsy is a prevalent neurological disorder characterised by repetitive seizures. It is categorised into three types: generalised epilepsy (GE), focal epilepsy (FE), and combined generalised and focal epilepsy. Correctly subtyping the epilepsy is important to select appropriate treatments. The types are mainly determined (i.e., diagnosed) by their semiologies supported by clinical examinations, such as electroencephalography and magnetoencephalography (MEG). Although these examinations are traditionally based on visual inspections of interictal epileptic discharges (IEDs), which are not always visible, alternative analyses have been anticipated. We examined if resting-state brain activities can distinguish patients with GE, which would help us to diagnose the type of epilepsy. METHODS The 5 min resting-state brain activities acquired using MEG were obtained retrospectively from 15 patients with GE. The cortical source of the activities was estimated at each frequency band from delta to high-frequency oscillation (HFO). These estimated activities were compared with reference datasets from 133 healthy individuals and control data from 29 patients with FE. RESULTS Patients with GE showed larger theta in the occipital, alpha in the left temporal, HFO in the rostral deep regions, and smaller HFO in the caudal ventral regions. Their area under the curves of the receiver operating characteristic curves was around 0.8-0.9. The distinctive pattern was not found for data from FE. CONCLUSION Patients with GE show distinctive resting-state brain activity, which could be a potential biomarker and used complementarily to classical analysis based on the visual inspection of IEDs.
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Affiliation(s)
- Galymzhan Issabekov
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Takahiro Matsumoto
- Department of Neurosurgery, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro 080-0833, Japan
| | - Keisuke Fukasawa
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Sayuri Ichikawa
- Clinical Laboratory, Kumagaya General Hospital, Kumagaya 360-8567, Japan
| | - Yoshihito Shigihara
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya 360-8567, Japan; Precision Medicine Centre, Hokuto Hospital, Obihiro 080-0833, Japan.
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4
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Zhang B, Lage-Rupprecht V, Wegner P, Sargsyan A, Gebel S, Jacobs M, Klein J, Hofmann-Apitius M, Tom Kodamullil A. Design of the formalized and integrated Alzheimer's Disease Ontology and its application in retrieving textual data via text mining. Database (Oxford) 2023; 2023:baad085. [PMID: 38041858 PMCID: PMC10693436 DOI: 10.1093/database/baad085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 11/04/2023] [Accepted: 11/15/2023] [Indexed: 12/04/2023]
Abstract
As one of the leading causes for dementia in the population, it is imperative that we discern exactly why Alzheimer's disease (AD) has a strong molecular association with beta-amyloid and tau. Although a clear understanding about etiology and pathogenesis of AD remains unsolved, scientists worldwide have dedicated significant efforts to discovering the molecular interactions linked to the pathological characteristics and potential treatments. Knowledge representations, such as domain ontologies encompassing our current understanding about AD, could greatly assist and contribute to disease research. This paper describes the construction and application of the integrated Alzheimer's Disease Ontology (ADO), combining selected concepts from the former version of the ADO and the Alzheimer's Disease Mapping Ontology (ADMO). In addition to the existing entities available from these knowledge models, essential knowledge about AD from public sources, such as newly discovered risk factor genes and novel treatments, was also integrated. The ADO can also be leveraged in text mining scenarios given that it is conceptually enriched with domain-specific knowledge as well as their relations. The integrated ADO consists of 39 855 total axioms. The ontology covers many aspects of the AD domain, including risk factor genes, clinical features, treatments and experimental models. The ontology complies with the Open Biological and Biomedical Ontology principles and was accepted by the foundry. In this paper, we illustrate the role of the presented ontology in extracting textual information from the SCAIView database and key measures in an ADO-based corpus. Database URL: https://academic.oup.com/database.
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Affiliation(s)
- Bide Zhang
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Vanessa Lage-Rupprecht
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Philipp Wegner
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Astghik Sargsyan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Stephan Gebel
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Marc Jacobs
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Jürgen Klein
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin 53754, Germany
- Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala 683503, India
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Blobel B, Ruotsalainen P, Oemig F, Giacomini M, Sottile PA, Endsleff F. Principles and Standards for Designing and Managing Integrable and Interoperable Transformed Health Ecosystems. J Pers Med 2023; 13:1579. [PMID: 38003894 PMCID: PMC10672117 DOI: 10.3390/jpm13111579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual's health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author's leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, 93053 Regensburg, Germany
- Faculty European Campus Rottal-Inn, Deggendorf Institute of Technology, 94469 Deggendorf, Germany
- First Medical Faculty, Charles University Prague, 11000 Staré Mĕsto, Czech Republic
| | - Pekka Ruotsalainen
- Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland;
| | - Frank Oemig
- IT-Consulting in Healthcare, 45472 Mülheim, Germany;
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16145 Genoa, Italy;
| | | | - Frederik Endsleff
- IT Architecture, Centre for IT and Medical Technology (CIMT), The Capital Region of Denmark, 2100 Copenhagen, Denmark;
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Tao S, Abeysinghe R, De La Esperanza BT, Lhatoo S, Zhang GQ, Cui L. Extracting Temporal Expressions of First Seizure Onset from Epilepsy Patient Discharge Summaries. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:515-524. [PMID: 37350927 PMCID: PMC10283149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Early onset of seizure is a potential risk factor for Sudden Unexpected Death in Epilepsy (SUDEP). However, the first seizure onset information is often documented as clinical narratives in epilepsy monitoring unit (EMU) discharge summaries. Manually extracting first seizure onset time from discharge summaries is time consuming and labor-intensive. In this work, we developed a rule-based natural language processing pipeline for automatically extracting the temporal information of patients' first seizure onset from EMU discharge summaries. We use the Epilepsy and Seizure Ontology (EpSO) as the core knowledge resource and construct 4 extraction rules based on 300 randomly selected EMU discharge summaries. To evaluate the effectiveness of the extraction pipeline, we apply the constructed rules on another 200 unseen discharge summaries and compare the results against the manual evaluation of a domain expert. Overall, our extraction pipeline achieved a precision of 0.75, recall of 0.651, and F1-score of 0.697. This is an encouraging initial result which will allow us to gain insights into potentially better-performing approaches.
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Affiliation(s)
- Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX
| | - Rashmie Abeysinghe
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX
| | | | - Samden Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX
| | - Licong Cui
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX
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Liu C, Talaei-Khoei A, Storey VC, Peng G. A Review of the State of the Art of Data Quality in Healthcare. JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2023. [DOI: 10.4018/jgim.316236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.
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Affiliation(s)
- Caihua Liu
- Guilin University of Electronic Technology, China
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8
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Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records. Sci Rep 2022; 12:19430. [PMID: 36371527 PMCID: PMC9653502 DOI: 10.1038/s41598-022-23101-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.
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The Representation of Causality and Causation with Ontologies: A Systematic Literature Review. Online J Public Health Inform 2022; 14:e4. [PMID: 36120162 PMCID: PMC9473331 DOI: 10.5210/ojphi.v14i1.12577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Objective To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. Methods We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. Results The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. Conclusion No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.
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10
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Li X, Tao S, Lhatoo SD, Cui L, Huang Y, Hampson JP, Zhang GQ. A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy. Front Big Data 2022; 5:965715. [PMID: 36059922 PMCID: PMC9428292 DOI: 10.3389/fdata.2022.965715] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/11/2022] [Indexed: 02/03/2023] Open
Abstract
Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Samden D. Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Licong Cui
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yan Huang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Johnson P. Hampson
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States,*Correspondence: Guo-Qiang Zhang
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11
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Prantzalos K, Zhang J, Shafiabadi N, Fernandez-BacaVaca G, Sahoo SS. Epilepsy-Connect: An Integrated Knowledgebase for Characterizing Alterations in Consciousness State of Pharmacoresistant Epilepsy Patients. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:1019-1028. [PMID: 35308974 PMCID: PMC8861706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alterations in consciousness state are a defining characteristic of focal epileptic seizures. Consequently, understanding the complex changes in neurocognitive networks which underpin seizure-induced alterations in consciousness state is important for advancement in seizure classification. Comprehension of these changes are complicated by a lack of data standardization; however, the use of a common terminological system or ontology in a patient registry minimizes this issue. In this paper, we introduce an integrated knowledgebase called Epilepsy-Connect to improve the understanding of changes in consciousness states during focal seizures of pharmacoresistant epilepsy patients. This registry catalogues over 809 seizures from 70 patients at University Hospital's Epilepsy Center who were undergoing stereotactic electroencephalography (SEEG) monitoring as part of an evaluation for surgical intervention. Although Epilepsy-Connect focuses on consciousness states, it aims to enable users to leverage data from an informatics platform to analyze epilepsy data in a streamlined manner. Epilepsy-Connect is available at https://bmhinformatics.case.edu/Epilepsyconnect/login/.
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Affiliation(s)
- Katrina Prantzalos
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jianzhe Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Nassim Shafiabadi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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12
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Müller B, Castro LJ, Rebholz-Schuhmann D. Ontology-based identification and prioritization of candidate drugs for epilepsy from literature. J Biomed Semantics 2022; 13:3. [PMID: 35073996 PMCID: PMC8785029 DOI: 10.1186/s13326-021-00258-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.
Results
We identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.
Conclusions
Biomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.
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13
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Tao S, Cui L, Chou WC, Lhatoo S, Zhang GQ. DaT3M: A Data Tracker for Multi-faceted Management of Multi-site Clinical Research Data Submission, Curation, Master Inventorying, and Sharing. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:466-475. [PMID: 35854726 PMCID: PMC9285149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, 629 MRI imaging datasets, 177 bio-chemistry datasets, 722 DNA datasets, 2,289 follow-up forms, and 30 SUDEP forms. Preliminary, structured, one-on-one usability evaluations were performed with 7 researchers from four institutions. System Usability Score reached 85.3, showing that DaT3M has achieved high levels of user satisfaction based on our pilot evaluation.
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Affiliation(s)
- Shiqiang Tao
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Licong Cui
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Wei-Chun Chou
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Samden Lhatoo
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Guo-Qiang Zhang
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
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14
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Lewis-Smith D, Galer PD, Balagura G, Kearney H, Ganesan S, Cosico M, O'Brien M, Vaidiswaran P, Krause R, Ellis CA, Thomas RH, Robinson PN, Helbig I. Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable. Epilepsia 2021; 62:1293-1305. [PMID: 33949685 PMCID: PMC8272408 DOI: 10.1111/epi.16908] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/19/2021] [Accepted: 04/01/2021] [Indexed: 01/08/2023]
Abstract
Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. Methods: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort’s data were provided in a different format and harmonized using the two versions of the HPO. Results: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals’ seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures.
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Affiliation(s)
- David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ganna Balagura
- Medical Genetics Unit, IRCSS Giannina Gaslini Institute, Genoa, Italy
| | - Hugh Kearney
- FutureNeuro the SFI Research Centre for Chronic and Rare Neurological Diseases, Royal College of Surgeons in Ireland, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mahgenn Cosico
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret O'Brien
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Priya Vaidiswaran
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK.,Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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15
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Li X, Cui L, Zhang GQ, Lhatoo SD. Can Big Data guide prognosis and clinical decisions in epilepsy? Epilepsia 2021; 62 Suppl 2:S106-S115. [PMID: 33529363 PMCID: PMC8011949 DOI: 10.1111/epi.16786] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 01/16/2023]
Abstract
Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Licong Cui
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Samden D. Lhatoo
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
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16
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Tao S, Lhatoo S, Hampson J, Cui L, Zhang GQ. A Bespoke Electronic Health Record for Epilepsy Care (EpiToMe): Development and Qualitative Evaluation. J Med Internet Res 2021; 23:e22939. [PMID: 33576745 PMCID: PMC7910122 DOI: 10.2196/22939] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/21/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND While electronic health records (EHR) bring various benefits to health care, EHR systems are often criticized as cumbersome to use, failing to fulfill the promise of improved health care delivery with little more than a means of meeting regulatory and billing requirements. EHR has also been recognized as one of the contributing factors for physician burnout. OBJECTIVE Specialty-specific EHR systems have been suggested as an alternative approach that can potentially address challenges associated with general-purpose EHRs. We introduce the Epilepsy Tracking and optimized Management engine (EpiToMe), an exemplar bespoke EHR system for epilepsy care. EpiToMe uses an agile, physician-centered development strategy to optimize clinical workflow and patient care documentation. We present the design and implementation of EpiToMe and report the initial feedback on its utility for physician burnout. METHODS Using collaborative, asynchronous data capturing interfaces anchored to a domain ontology, EpiToMe distributes reporting and documentation workload among technicians, clinical fellows, and attending physicians. Results of documentation are transmitted to the parent EHR to meet patient care requirements with a push of a button. An HL7 (version 2.3) messaging engine exchanges information between EpiToMe and the parent EHR to optimize clinical workflow tasks without redundant data entry. EpiToMe also provides live, interactive patient tracking interfaces to ease the burden of care management. RESULTS Since February 2019, 15,417 electroencephalogram reports, 2635 Epilepsy Monitoring Unit daily reports, and 1369 Epilepsy Monitoring Unit phase reports have been completed in EpiToMe for 6593 unique patients. A 10-question survey was completed by 11 (among 16 invited) senior clinical attending physicians. Consensus was found that EpiToMe eased the burden of care documentation for patient management, a contributing factor to physician burnout. CONCLUSIONS EpiToMe offers an exemplar bespoke EHR system developed using a physician-centered design and latest advancements in information technology. The bespoke approach has the potential to ease the burden of care management in epilepsy. This approach is applicable to other clinical specialties.
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Affiliation(s)
- Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States.,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Samden Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States.,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Johnson Hampson
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States.,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Licong Cui
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States.,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States.,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States.,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
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17
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Porat Rein A, Kramer U, Hausman Kedem M, Fattal-Valevski A, Mitelpunkt A. Early prediction of encephalopathic transformation in children with benign epilepsy with centro-temporal spikes. Brain Dev 2021; 43:268-279. [PMID: 32912653 DOI: 10.1016/j.braindev.2020.08.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/06/2020] [Accepted: 08/18/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Most children with Benign epilepsy with centro-temporal spikes (BECTS) undergo remission during late adolescence and do not require treatment. In a small group of patients, the condition may evolve to encephalopathic syndromes including epileptic encephalopathy with continuous spike-and-wave during sleep (ECSWS), or Landau-Kleffner Syndrome (LKS). Development of prediction models for early identification of at-risk children is of utmost importance. AIM To develop a predictive model of encephalopathic transformation using data-driven approaches, reveal complex interactions to identify potential risk factors. METHODS Data were collected from a cohort of 91 patients diagnosed with BECTS treated between the years 2005-2017 at a pediatric neurology institute. Data on the initial presentation was collected based on a novel BECTS ontology and used to discover potential risk factors and to build a predictive model. Statistical and machine learning methods were compared. RESULTS A subgroup of 18 children had encephalopathic transformation. The least absolute shrinkage and selection operator (LASSO) regression Model with Elastic Net was able to successfully detect children with ECSWS or LKS. Sensitivity and specificity were 0.83 and 0.44. The most notable risk factors were fronto-temporal and temporo-parietal localization of epileptic foci, semiology of seizure involving dysarthria or somatosensory auras. CONCLUSION Novel prediction model for early identification of patients with BECTS at risk for ECSWS or LKS. This model can be used as a screening tool and assist physicians to consider special management for children predicted at high-risk. Clinical application of machine learning methods opens new frontiers of personalized patient care and treatment.
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Affiliation(s)
- Adi Porat Rein
- Sackler Faculty of Medicine, Tel-Aviv University, Israel.
| | - Uri Kramer
- Sackler Faculty of Medicine, Tel-Aviv University, Israel; Pediatric Neurology Institute, Dana-Dwek Children's Hospital, Tel Aviv Sourasky Medical Center, Israel
| | - Moran Hausman Kedem
- Sackler Faculty of Medicine, Tel-Aviv University, Israel; Pediatric Neurology Institute, Dana-Dwek Children's Hospital, Tel Aviv Sourasky Medical Center, Israel
| | - Aviva Fattal-Valevski
- Sackler Faculty of Medicine, Tel-Aviv University, Israel; Pediatric Neurology Institute, Dana-Dwek Children's Hospital, Tel Aviv Sourasky Medical Center, Israel
| | - Alexis Mitelpunkt
- Sackler Faculty of Medicine, Tel-Aviv University, Israel; Pediatric Neurology Institute, Dana-Dwek Children's Hospital, Tel Aviv Sourasky Medical Center, Israel
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18
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Sahoo SS, Gershon A, Nassim S, Kaushik G, Curtis T, Lhatoo SD, Fernandez-BacaVaca G. NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1090-1099. [PMID: 33936485 PMCID: PMC8075544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Objective: Brain functional connectivity measures are often used to study interactions between brain regions in various neurological disorders such as epilepsy. In particular, functional connectivity measures derived from high resolution electrophysiological signal data have been used to characterize epileptic networks in epilepsy patients. However, existing signal data formats as well as computational methods are not suitable for complex multi-step methods used for processing and analyzing signal data across multiple seizure events. To address the significant data management challenges associated with signal data, we have developed a new workflow-based tool called NeuroIntegrative Connectivity (NIC) using the Cloudwave Signal Format (CSF) as a common data abstraction model. Method: The NIC compositional workflow-based tool consists of: (1) Signal data processing component for automated pre- processing and generation of CSF files with semantic annotation using epilepsy domain ontology; and (2) Functional network computation component for deriving functional connectivity metrics from signal data analysis across multiple recording channels. The NIC tool streamlines signal data management using a modular software implementation architecture that supports easy extension with new libraries of signal coupling measures and fast data retrieval using a binary search tree indexing structure called NIC-Index. Result and Conclusion: We evaluated the NIC tool by processing and analyzing signal data for 28 seizure events in two patients with refractory epilepsy. The result shows that certain brain regions have high local measure of connectivity, such as total degree, as compared to other regions during ictal events in both patients. In addition, global connectivity measures, which characterize transitivity and efficiency, increase in value during the initial period of the seizure followed by decrease towards the end of seizure. The NIC tool allows users to efficiently apply several network analysis metrics to study global and local changes in epileptic networks in patient cohort studies.
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Affiliation(s)
- Satya S Sahoo
- epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Arthur Gershon
- epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Shafiabadi Nassim
- epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Ghosh Kaushik
- Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Tatsuoka Curtis
- epartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Sciences Center, Houston, TX, USA
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Kanza S, Graham Frey J. Semantic Technologies in Drug Discovery. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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20
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Lhatoo SD, Bernasconi N, Blumcke I, Braun K, Buchhalter J, Denaxas S, Galanopoulou A, Josephson C, Kobow K, Lowenstein D, Ryvlin P, Schulze-Bonhage A, Sahoo SS, Thom M, Thurman D, Worrell G, Zhang GQ, Wiebe S. Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia 2020; 61:1869-1883. [PMID: 32767763 DOI: 10.1111/epi.16633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 12/25/2022]
Abstract
Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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Affiliation(s)
- Samden D Lhatoo
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ingmar Blumcke
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Kees Braun
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeffrey Buchhalter
- Department of Neurology, St Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Aristea Galanopoulou
- Saul Korey Department of Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Colin Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
| | - Katja Kobow
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Daniel Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Philippe Ryvlin
- Department of Neurosciences, University of Lausanne, Lausanne, Switzerland
| | | | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Maria Thom
- Institute of Neurology, University College London, London, UK
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Guo-Qiang Zhang
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Samuel Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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21
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Chiang KL, Huang CY, Hsieh LP, Chang KP. A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome. Epilepsy Behav 2020; 106:107021. [PMID: 32224446 DOI: 10.1016/j.yebeh.2020.107021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 03/02/2020] [Accepted: 03/02/2020] [Indexed: 01/01/2023]
Abstract
PURPOSE The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. METHODS We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. RESULTS Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis. CONCLUSION We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.
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Affiliation(s)
- Kuo-Liang Chiang
- Department of Pediatric Neurology, Kuang-Tien General Hospital, No. 117, Shatian Road, Shalu District, Taichung 43303, Taiwan; Department of Nutrition, Hungkuang University, No. 1018, Section 6, Taiwan Boulevard, Shalu District, Taichung 43302, Taiwan; Department of Industrial Engineering and Enterprise Information, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan.
| | - Chin-Yin Huang
- Department of Industrial Engineering and Enterprise Information, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan; Program for Health Administration, Tunghai University, P.O. Box 985, Taichung 40704, Taiwan.
| | - Liang-Po Hsieh
- Department of Neurology, Cheng-Ching Hospital, No. 966, Section 4, Taiwan Boulevard, Xitun District, Taichung 40764, Taiwan
| | - Kai-Ping Chang
- Department of Pediatric Neurology, Taipei Veterans General Hospital, No.201, Section 2, Shipai Rd., Beitou District, Taipei 11217, Taiwan
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22
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Hong X, Liu C, Momotaz H, Cassidy K, Sajatovic M, Sahoo SS. Enhancing Multi-Center Patient Cohort Studies in the Managing Epilepsy Well (MEW) Network: Integrated Data Integration and Statistical Analysis. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:1071-1080. [PMID: 32308904 PMCID: PMC7153120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Self-management techniques that assist patients with chronic conditions, such as epilepsy, diabetes, and arthritis, play an important role in managing and caring for their conditions. The US Center for Disease Control and Prevention (CDC)-funded Managing Epilepsy Well (MEW) Network consists of 11 study sites across the US that aims to develop and disseminate self-management techniques for epilepsy patients. Epilepsy affects more than 65 million patients worldwide with serious negative impact on their own as well as their family member's quality of life. Taking advantage of advances in biomedical informatics, the MEW Network has created an integrated database (MEW DB) using a common data model and two tiers of study variables. The MEW DB consists of 1680 patient data records covering a wide range of patient population nationwide. Therefore, there is growing interest in the use of the MEW DB for different cohort query analysis. To address the challenges in: (1) selecting appropriate MEW research studies based on inclusion/exclusion criteria; (2) creating a patient cohort for given research hypothesis; and (3) performing appropriate statistical tests; we have developed an integrated data query and statistical analysis informatics tool called Insight. The Insight platform features an intuitive user interface to support the three phases of study selection, patient cohort creation, and statistical testing with the use of an epilepsy domain ontology to support ontology-driven query expansion. We evaluate the Insight platform using two user evaluation methods of "first click testing" and "user satisfaction survey". In addition, we performed a time performance test of the Insight platform using four patient datasets and three statistical test. The results of the user evaluation show that Insight platform is strongly approved by the users and the results of the time performance show that there is marginal difference in performance as the volume of patient data increases in the MEW DB.
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Affiliation(s)
- Xinting Hong
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Chang Liu
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Hasina Momotaz
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Kristin Cassidy
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Martha Sajatovic
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Satya S Sahoo
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
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23
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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24
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Helbig I, Ellis CA. Personalized medicine in genetic epilepsies - possibilities, challenges, and new frontiers. Neuropharmacology 2020; 172:107970. [PMID: 32413583 DOI: 10.1016/j.neuropharm.2020.107970] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 01/05/2020] [Accepted: 01/16/2020] [Indexed: 12/13/2022]
Abstract
Identifying the optimal treatment based on specific characteristics of each patient is the main promise of precision medicine. In the field of epilepsy, the identification of more than 100 causative genes provides the enticing possibility of treatments targeted to specific disease etiologies. These conditions include classical examples, such as the use of vitamin B6 in antiquitin deficiency or the ketogenic diet in GLUT1 deficiency, where the disease mechanism can be directly addressed by the selection of a specific therapeutic compound. For epilepsies caused by channelopathies there have been advances in understanding how the selection of existing medications can be targeted to the functional consequences of genetic alterations. We discuss the examples of the use of sodium channel blockers such as phenytoin and oxcarbazepine in the sodium channelopathies, quinidine in KCNT1-related epilepsies, and strategies in GRIN-related epilepsies as examples of epilepsy precision medicine. Assessing the clinical response to targeted treatments of these conditions has been complicated by genetic and phenotypic heterogeneity, as well as by various neurological and non-neurological comorbidities. Moving forward, the development of standardized outcome measures will be critical to successful precision medicine trials in complex and heterogeneous disorders like the epilepsies. Finally, we address new frontiers in epilepsy precision medicine, including the need to match the growing volume of genetic data with high-throughput functional assays to assess the functional consequences of genetic variants and the ability to extract clinical data at large scale from electronic medical records and apply quantitative methods based on standardized phenotyping language.
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Affiliation(s)
- Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
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25
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Baldassano SN, Hill CE, Shankar A, Bernabei J, Khankhanian P, Litt B. Big data in status epilepticus. Epilepsy Behav 2019; 101:106457. [PMID: 31444029 PMCID: PMC6944751 DOI: 10.1016/j.yebeh.2019.106457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".
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Affiliation(s)
- Steven N. Baldassano
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Chloé E. Hill
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Arjun Shankar
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - John Bernabei
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Pouya Khankhanian
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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26
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Maldonado R, Harabagiu SM. Active deep learning for the identification of concepts and relations in electroencephalography reports. J Biomed Inform 2019; 98:103265. [PMID: 31470094 DOI: 10.1016/j.jbi.2019.103265] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 06/23/2019] [Accepted: 08/04/2019] [Indexed: 11/15/2022]
Abstract
The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. However, the recognition of multiple types of medical concepts, along with the many attributes characterizing them is challenging, and so is the recognition of the possible relations between them, especially when desiring to make use of active learning. To address these challenges, in this paper we present the Self-Attention Concept, Attribute and Relation (SACAR) identifier, which relies on a powerful encoding mechanism based on the recently introduced Transformer neural architecture (Dehghani et al., 2018). The SACAR identifier enabled us to consider a recently introduced framework for active learning which uses deep imitation learning for its selection policy. Our experimental results show that SACAR was able to identify medical concepts more precisely and exhibited enhanced recall, compared with previous methods. Moreover, SACAR achieves superior performance in attribute classification for attribute categories of interest, while identifying the relations between concepts with performance competitive with our previous techniques. As a multi-task network, SACAR achieves this performance on the three prediction tasks simultaneously, with a single, complex neural network. The learning curves obtained in the active learning process when using the novel Active Learning Policy Neural Network (ALPNN) show a significant increase in performance as the active learning progresses. These promising results enable the extraction of clinical knowledge available in a large collection of EEG reports.
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Affiliation(s)
- Ramon Maldonado
- Human Language Technology Research Institute, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA.
| | - Sanda M Harabagiu
- Human Language Technology Research Institute, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA.
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27
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Barbour K, Hesdorffer DC, Tian N, Yozawitz EG, McGoldrick PE, Wolf S, McDonough TL, Nelson A, Loddenkemper T, Basma N, Johnson SB, Grinspan ZM. Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing. Epilepsia 2019; 60:1209-1220. [PMID: 31111463 DOI: 10.1111/epi.15966] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/25/2019] [Accepted: 04/25/2019] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of using regular expressions to identify risk factors in EMRs and (2) barriers to generalizability. METHODS Data included physician notes for 3000 patients from one medical center (home) and 1000 from five additional centers (away). Through chart review, we identified three SUDEP risk factors: (1) generalized tonic-clonic seizures, (2) refractory epilepsy, and (3) epilepsy surgery candidacy. Regular expressions of risk factors were manually created with home training data, and performance was evaluated with home test and away test data. Performance was evaluated by sensitivity, positive predictive value, and F-measure. Generalizability was defined as an absolute decrease in performance by <0.10 for away versus home test data. To evaluate underlying barriers to generalizability, we identified causes of errors seen more often in away data than home data. To demonstrate how small revisions can improve generalizability, we removed three "boilerplate" standard text phrases from away notes and repeated performance. RESULTS We observed high performance in home test data (F-measure range = 0.86-0.90), and low to high performance in away test data (F-measure range = 0.53-0.81). After removing three boilerplate phrases, away performance improved (F-measure range = 0.79-0.89) and generalizability was achieved for nearly all measures. The only significant barrier to generalizability was use of boilerplate phrases, causing 104 of 171 errors (61%) in away data. SIGNIFICANCE Regular expressions are a feasible and probably a generalizable method to identify variables related to SUDEP risk. Our methods may be implemented to create large patient cohorts for research and to generate electronic prompts for SUDEP counseling.
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Affiliation(s)
- Kristen Barbour
- Division of Child Neurology, Weill Cornell Medicine, New York, New York
| | - Dale C Hesdorffer
- Department of Epidemiology, Columbia University Medical Center, New York, New York
| | - Niu Tian
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Elissa G Yozawitz
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York
| | | | - Steven Wolf
- Department of Neurology, Mount Sinai Health System, New York, New York
| | - Tiffani L McDonough
- Department of Epidemiology, Columbia University Medical Center, New York, New York
| | - Aaron Nelson
- Department of Neurology, New York University Langone Medical Center, New York, New York
| | | | - Natasha Basma
- Division of Child Neurology, Weill Cornell Medicine, New York, New York
| | - Stephen B Johnson
- Division of Child Neurology, Weill Cornell Medicine, New York, New York
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28
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Gershon A, Devulapalli P, Zonjy B, Ghosh K, Tatsuoka C, Sahoo SS. Computing Functional Brain Connectivity in Neurological Disorders: Efficient Processing and Retrieval of Electrophysiological Signal Data. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2019; 2019:107-116. [PMID: 31258962 PMCID: PMC6568074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Brain functional network connectivity is an important measure for characterizing changes in a variety of neurological disorders, for example Alzheimer's Disease, Parkinson Disease, and Epilepsy. Epilepsy is a serious neurological disorder affecting more than 50 million persons worldwide with severe impact on the quality of life of patients and their family members due to recurrent seizures. More than 30% of epilepsy patients are refractive to pharmacotherapy and are considered for resection to disrupt epilepsy seizure networks. However, 20-50% of these patients continue to have seizures after surgery. Therefore, there is a critical need to gain new insights into the characteristics of epilepsy seizure networks involving one of more brain regions and accurately delineate epileptogenic zone as a target for surgery. Although there is growing availability of large volume of high resolution stereotactic electroencephalogram (SEEG) data recorded from intracranial electrodes during presurgical evaluation of patients, there are significant informatics challenges associated with processing and analyzing this large signal dataset for characterizing epilepsy seizure networks. In this paper, we describe the development and application of a high-performance indexing structure for efficient retrieval of large-scale SEEG signal data to compute seizure network patterns corresponding to brain functional connectivity networks. This novel Neuro-Integrative Connectivity (NIC) search and retrieval method has been developed by extending the red-black tree index model together with an efficient lookup algorithm. We systematically perform a comparative evaluation of the proposed NIC index using de-identified SEEG data from a patient with temporal lobe epilepsy to retrieve segments of signal data corresponding to multiple seizure events and demonstrate the significant advantages of the NIC index as compared to existing methods. This new NIC Index enables faster computation of brain functional connectivity measures in epilepsy patients for large-scale network analysis and potentially provide new insights into the organization as well as evolution of seizure networks in epilepsy patients.
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Affiliation(s)
- Arthur Gershon
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Pramith Devulapalli
- Department of Neurology, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Bilal Zonjy
- Department of Neurology, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Kaushik Ghosh
- Department of Mathematical Sciences, College of Sciences, University of Nevada, Las Vegas, NV, U.S.A
| | - Curtis Tatsuoka
- Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH
- Department of Neurology, School of Medicine, Case Western Reserve University, Cleveland, OH
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29
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Goodwin TR, Harabagiu SM. Learning relevance models for patient cohort retrieval. JAMIA Open 2018; 1:265-275. [PMID: 30474078 PMCID: PMC6241510 DOI: 10.1093/jamiaopen/ooy010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 02/26/2018] [Accepted: 09/05/2018] [Indexed: 12/23/2022] Open
Abstract
Objective We explored how judgements provided by physicians can be used to learn relevance models that enhance the quality of patient cohorts retrieved from Electronic Health Records (EHRs) collections. Methods A very large number of features were extracted from patient cohort descriptions as well as EHR collections. The features were used to investigate retrieving (1) neurology-specific patient cohorts from the de-identified Temple University Hospital electroencephalography (EEG) Corpus as well as (2) the more general cohorts evaluated in the TREC Medical Records Track (TRECMed) from the de-identified hospital records provided by the University of Pittsburgh Medical Center. The features informed a learning relevance model (LRM) that took advantage of relevance judgements provided by physicians. The LRM implements a pairwise learning-to-rank framework, which enables our learning patient cohort retrieval (L-PCR) system to learn from physicians' feedback. Results and Discussion We evaluated the L-PCR system against state-of-the-art traditional patient cohort retrieval systems, and observed a 27% improvement when operating on EEGs and a 53% improvement when operating on TRECMed EHRs, showing the promise of the L-PCR system. We also performed extensive feature analyses to reveal the most effective strategies for representing cohort descriptions as queries, encoding EHRs, and measuring cohort relevance. Conclusion The L-PCR system has significant promise for reliably retrieving patient cohorts from EHRs in multiple settings when trained with relevance judgments. When provided with additional cohort descriptions, the L-PCR system will continue to learn, thus offering a potential solution to the performance barriers of current cohort retrieval systems.
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Affiliation(s)
- Travis R Goodwin
- Department of Computer Science, Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas, USA
| | - Sanda M Harabagiu
- Department of Computer Science, Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas, USA
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30
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Feng J, Feng L, Zhang G. Mitochondrial damage in hippocampal neurons of rats with epileptic protein expression of Fas and caspase-3. Exp Ther Med 2018; 16:2483-2489. [PMID: 30210599 PMCID: PMC6122536 DOI: 10.3892/etm.2018.6439] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 06/13/2018] [Indexed: 11/06/2022] Open
Abstract
Epilepsy model in rats was established to observe the behavior and pathological changes, and to detect mitochondrial dysfunction, exploring its possible molecular mechanisms. The epileptic status of Sprague-Dawley (SD) rats was induced by intraperitoneal injection of lithium chloride, and the change of behavior was recorded. Electroencephalogram (EEG) was used to measure the abnormal discharge of neurons in rats. The brain tissue was fixed with polyformaldehyde and the paraffin sections were prepared, and the damage of the hippocampal neurons was observed with Nissl staining. Mitochondrial ATP and mitochondrial DNA were examined to assess mitochondrial dysfunction. Finally, qPCR and western blot analysis were used to detect mRNA and protein expression of fatty acid synthetase (Fas), Fas ligand (FasL) and caspase-3 in rat hippocampal neurons. The correlation between the mitochondrial dysfunction of rat hippocampal neurons and Fas and caspase-3 was analyzed. Compared with the normal group rats, the model group showed typical seizures, which were determined by the Racine attack score. EEG of the hippocampus of the model group was recorded in cluster in model group rats. Nissl staining showed a different degree of damage to the hippocampal neurons in the model group compared with normal rats. The mitochondrial ATP content and DNA content of rat hippocampal neurons in the model group were significantly lower than that of normal rats (P<0.01). The qPCR and western blot results showed that the mRNA and protein expression levels of Fas, FasL and caspase-3 were significantly increased in the hippocampus of rat model group (P<0.01). The expression level of Fas and caspase-3 in hippocampal tissues of rats was negatively correlated with mitochondrial DNA content. In conclusion, seizures cause damage of neuron mitochondria in rat hippocampus leading to death of hippocampal neurons, the mitochondrial damage of hippocampal neurons in epileptic rats was closely related to the expression of Fas and caspase-3.
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Affiliation(s)
- Junqiang Feng
- Department of Neurology, Daqing Longnan Hospital, Daqing, Heilongjiang 163453, P.R. China
| | - Lifang Feng
- Department of Family Practice, Binzhou People's Hospital, Binzhou, Shandong 256600, P.R. China
| | - Guiru Zhang
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250022, P.R. China
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Maldonado R, Goodwin TR, Harabagiu SM. Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:156-165. [PMID: 29888063 PMCID: PMC5961777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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 frame work 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 validationF1 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.
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Maldonado R, Goodwin TR, Skinner MA, Harabagiu SM. Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2017:1233-1242. [PMID: 29854192 PMCID: PMC5977726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in deep learning are able to capture high-quality knowledge from textual data and represent it in graphical structures. As opposed to the top-down methodology used in the generation of ontologies, which starts with the principled design of the upper ontology, the bottom-up methodology enabled by deep learning encodes the likelihood that concepts share certain relations, as evidenced by data. In this paper, we present a knowledge representation produced by deep learning methods, called Medical Knowledge Embeddings (MKE), that encode medical concepts related to the study of epilepsy and the relations between them. Many of the epilepsy-relevant medical concepts from MKE are not yet available in existing biomedical ontologies, but are mentioned in vast collections of epilepsy-related medical records which also imply their relationships. The evaluation of the MKE indicates high accuracy of the medical concepts automatically identified from clinical text as well as promising results in terms of correctness and completeness of relations produced by deep learning.
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Affiliation(s)
| | | | - Michael A Skinner
- The University of Texas at Dallas, Richardson, TX
- The University of Texas Southwestern Medical Center, Department of Surgery, Dallas, TX
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Grinspan ZM, Tian N, Yozawitz EG, McGoldrick PE, Wolf SM, McDonough TL, Nelson A, Hafeez B, Johnson SB, Hesdorffer DC. Common terms for rare epilepsies: Synonyms, associated terms, and links to structured vocabularies. Epilepsia Open 2018; 3:91-97. [PMID: 29588993 PMCID: PMC5839304 DOI: 10.1002/epi4.12095] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2017] [Indexed: 11/28/2022] Open
Abstract
Identifying individuals with rare epilepsy syndromes in electronic data sources is difficult, in part because of missing codes in the International Classification of Diseases (ICD) system. Our objectives were the following: (1) to describe the representation of rare epilepsies in other medical vocabularies, to identify gaps; and (2) to compile synonyms and associated terms for rare epilepsies, to facilitate text and natural language processing tools for cohort identification and population‐based surveillance. We describe the representation of 33 epilepsies in 3 vocabularies: Orphanet, SNOMED‐CT, and UMLS‐Metathesaurus. We compiled terms via 2 surveys, correspondence with parent advocates, and review of web resources and standard vocabularies. UMLS‐Metathesaurus had entries for all 33 epilepsies, Orphanet 32, and SNOMED‐CT 25. The vocabularies had redundancies and missing phenotypes. Emerging epilepsies (SCN2A‐, SCN8A‐, KCNQ2‐, SLC13A5‐, and SYNGAP‐related epilepsies) were underrepresented. Survey and correspondence respondents included 160 providers, 375 caregivers, and 11 advocacy group leaders. Each epilepsy syndrome had a median of 15 (range 6–28) synonyms. Nineteen had associated terms, with a median of 4 (range 1–41). We conclude that medical vocabularies should fill gaps in representation of rare epilepsies to improve their value for epilepsy research. We encourage epilepsy researchers to use this resource to develop tools to identify individuals with rare epilepsies in electronic data sources.
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Affiliation(s)
| | - Niu Tian
- Centers for Disease Control and Prevention Atlanta Georgia U.S.A
| | | | | | | | | | - Aaron Nelson
- New York University Langone Medical Center New York New York U.S.A
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Serhani MA, Menshawy ME, Benharref A, Harous S, Navaz AN. New algorithms for processing time-series big EEG data within mobile health monitoring systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 149:79-94. [PMID: 28802332 DOI: 10.1016/j.cmpb.2017.07.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 06/05/2017] [Accepted: 07/18/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. METHODS In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. RESULTS We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. CONCLUSIONS Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.
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Affiliation(s)
- Mohamed Adel Serhani
- College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE.
| | - Mohamed El Menshawy
- Concordia Institute for Information Systems Engineering, Concordia University, 1515 Rue Sainte-Catherine O, Montréal, QC, Canada, H3G 2W1, Canada.
| | | | - Saad Harous
- College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE.
| | - Alramzana Nujum Navaz
- College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE.
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Maldonado R, Goodwin TR, Harabagiu SM. Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:229-238. [PMID: 28815135 PMCID: PMC5543351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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.
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Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, Forshee R, Walderhaug M, Botsis T. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform 2017; 73:14-29. [PMID: 28729030 DOI: 10.1016/j.jbi.2017.07.012] [Citation(s) in RCA: 271] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 06/07/2017] [Accepted: 07/14/2017] [Indexed: 12/24/2022]
Abstract
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.
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Affiliation(s)
- Kory Kreimeyer
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.
| | - Matthew Foster
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Abhishek Pandey
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Nina Arya
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Gwendolyn Halford
- FDA Library, US Food and Drug Administration, Silver Spring, MD, United States
| | - Sandra F Jones
- Cancer Surveillance Branch, Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Richard Forshee
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Mark Walderhaug
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Taxiarchis Botsis
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
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The importance of assessing behaviour and cognition in antiepileptic drug trials in children and adolescents. Acta Neurol Belg 2017; 117:425-432. [PMID: 28000064 DOI: 10.1007/s13760-016-0734-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 12/07/2016] [Indexed: 10/20/2022]
Abstract
It has long been recognised that uncontrolled childhood epilepsy is detrimental to cognition and behaviour, impacting on a patient's ability to succeed academically. Patients who experience more frequent and serious seizures are at greater risk for cognitive decline, emphasising the need for more effective epilepsy treatments to bring seizures under control. That said, although more effective antiepileptic drugs (AEDs) have the potential to limit the impact of uncontrolled seizures on cognitive and behavioural function, recently it has been acknowledged that deficits in these functions may be caused by AEDs themselves. The cognitive and behavioural effects of older-generation AEDs have been determined largely from AE reporting rather than from specific assessment. Recently, clinical trials of newer-generation AEDs, such as topiramate, levetiracetam and perampanel, have included standardised neuropsychological tests as outcome measures to assess their impact on cognition and behaviour in children and adolescents. However, to understand how we may limit the cognitive and behavioural side effects of AEDs, it is necessary for us to gain a fuller, more accurate, characterisation of their true impact. Such insight will depend on sophisticated and standardised approaches to the design of AED clinical trials. This review provides a general overview of our current understanding of the impact of both epilepsy and AEDs on cognition and behaviour, before focusing on the AEDs for which more detailed assessment, using standardised cognitive and behavioural measures, has been undertaken. We will then go on to discuss the key elements in the design of future AED clinical trials to address current unmet needs.
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38
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Sahoo SS, Valdez J, Rueschman M. Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:1070-1079. [PMID: 28269904 PMCID: PMC5333253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources. The National Institutes of Health (NIH) recently published a systematic guideline titled "Rigor and Reproducibility " for supporting reproducible research studies, which has also been accepted by several scientific journals. These journals will require published articles to conform to these new guidelines. Provenance metadata describes the history or origin of data and it has been long used in computer science to capture metadata information for ensuring data quality and supporting scientific reproducibility. In this paper, we describe the development of Provenance for Clinical and healthcare Research (ProvCaRe) framework together with a provenance ontology to support scientific reproducibility by formally modeling a core set of data elements representing details of research study. We extend the PROV Ontology (PROV-O), which has been recommended as the provenance representation model by World Wide Web Consortium (W3C), to represent both: (a) data provenance, and (b) process provenance. We use 124 study variables from 6 clinical research studies from the National Sleep Research Resource (NSRR) to evaluate the coverage of the provenance ontology. NSRR is the largest repository of NIH-funded sleep datasets with 50,000 studies from 36,000 participants. The provenance ontology reuses ontology concepts from existing biomedical ontologies, for example the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to model the provenance information of research studies. The ProvCaRe framework is being developed as part of the Big Data to Knowledge (BD2K) data provenance project.
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Affiliation(s)
- Satya S Sahoo
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Joshua Valdez
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Michael Rueschman
- Department of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
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Cui L, Huang Y, Tao S, Lhatoo SD, Zhang GQ. ODaCCI: Ontology-guided Data Curation for Multisite Clinical Research Data Integration in the NINDS Center for SUDEP Research. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:441-450. [PMID: 28269839 PMCID: PMC5333343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death. The Center for SUDEP Research (CSR) is an NINDS-funded Center Without Wall's initiative aimed at prospectively creating a comprehensive clinical research resource for SUDEP. This resource consists of a growing set of data and biological samples of a statistically significant cohort of patients at an elevated risk, best represented by the Epilepsy Monitoring Unit (EMU) patient population. The Informatics and Data Analytics Core (IDAC) of CSR has developed a state-of-the- art informatics infrastructure, to integrate patient data captured in multiple EMU's at a greatly accelerated pace. Data quality assurance is a priority of IDAC. This paper reports our approach, Ontology-guided Data Curation for Multisite Clinical Research Data Integration (ODaCCI), to address the challenging task of centralized data curation while new data is continuously generated and integrated from distributed sites. ODaCCI leverages the Epilepsy and Seizure Ontology not only for upstream data capture, but also for supporting a range of quality assurance tasks such as data quality monitoring, data update, and data reports. Between October 2014 and February 2016, ODaCCI has integrated phenotypic and electroencephalogram signal data of 629 patients from 7 clinical sites, while supporting continuous and asynchronous data quality enhancement overtime.
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Affiliation(s)
- Licong Cui
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY; Center for SUDEP Research (NINDS-funded Center Without Walls for Collaborative Research in the Epilepsies), Cleveland, OH
| | - Yan Huang
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY; Center for SUDEP Research (NINDS-funded Center Without Walls for Collaborative Research in the Epilepsies), Cleveland, OH
| | - Shiqiang Tao
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY
| | - Samden D Lhatoo
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY
| | - Guo-Qiang Zhang
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Institute of Biomedical Informatics, University of Kentucky, Lexington, KY
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40
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Goodwin TR, Harabagiu SM. Multi-modal Patient Cohort Identification from EEG Report and Signal Data. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:1794-1803. [PMID: 28269938 PMCID: PMC5333290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Clinical electroencephalography (EEG) is the most important investigation in the diagnosis and management of epilepsies. An EEG records the electrical activity along the scalp and measures spontaneous electrical activity of the brain. Because the EEG signal is complex, its interpretation is known to produce moderate inter-observer agreement among neurologists. This problem can be addressed by providing clinical experts with the ability to automatically retrieve similar EEG signals and EEG reports through a patient cohort retrieval system operating on a vast archive of EEG data. In this paper, we present a multi-modal EEG patient cohort retrieval system called MERCuRY which leverages the heterogeneous nature of EEG data by processing both the clinical narratives from EEG reports as well as the raw electrode potentials derived from the recorded EEG signal data. At the core of MERCuRY is a novel multimodal clinical indexing scheme which relies on EEG data representations obtained through deep learning. The index is used by two clinical relevance models that we have generated for identifying patient cohorts satisfying the inclusion and exclusion criteria expressed in natural language queries. Evaluations of the MERCuRY system measured the relevance of the patient cohorts, obtaining MAP scores of 69.87% and a NDCG of 83.21%.
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Hampel H, O’Bryant SE, Durrleman S, Younesi E, Rojkova K, Escott-Price V, Corvol JC, Broich K, Dubois B, Lista S. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling. Climacteric 2017; 20:107-118. [DOI: 10.1080/13697137.2017.1287866] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- H. Hampel
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. E. O’Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - S. Durrleman
- ARAMIS Lab, Inria Paris, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, Paris, France
| | - E. Younesi
- European Society for Translational Medicine, Vienna, Austria
| | - K. Rojkova
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - V. Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - J-C. Corvol
- Département de Neurologie, Sorbonne Université, Université Pierre et Marie Curie, Paris 06 UMR S 1127, Institut National de Santé et en Recherche Médicale (INSERM) U 1127 and CIC-1422, Centre National de Recherche Scientifique U 7225, Institut du Cerveau et de la Moelle Epinière, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France
| | - K. Broich
- President, Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - B. Dubois
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. Lista
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
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Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1028:17-37. [PMID: 29058214 DOI: 10.1007/978-981-10-6041-0_2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.
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Daniel C, Ouagne D, Sadou E, Paris N, Hussain S, Jaulent M, Kalra D. Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services. Learn Health Syst 2017; 1:e10014. [PMID: 31245551 PMCID: PMC6516724 DOI: 10.1002/lrh2.10014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 07/07/2016] [Accepted: 07/28/2016] [Indexed: 12/15/2022] Open
Abstract
With the development of platforms enabling the integration and use of phenome, genome, and exposome data in the context of international research, data management challenges are increasing, and scalable solutions for cross border and cross domain semantic interoperability need to be developed. Reusing routinely collected clinical data, especially, requires computable portable phenotype algorithms running across different electronic health record (EHR) products and healthcare systems. We propose a framework for describing and comparing mediation platforms enabling cross border phenotype identification within federated EHRs. This framework was used to describe the experience gained during the EHR4CR project and the evaluation of the platform developed for accessing semantically equivalent data elements across 11 European participating EHR systems from 5 countries. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data.
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Affiliation(s)
- Christel Daniel
- Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICSF‐75006ParisFrance
- AP‐HPParisFrance
| | - David Ouagne
- Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICSF‐75006ParisFrance
| | - Eric Sadou
- Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICSF‐75006ParisFrance
- AP‐HPParisFrance
| | | | - Sajjad Hussain
- Sorbonne Universités, UPMC Univ Paris 06, INSERM UMR_S 1142, LIMICSF‐75006ParisFrance
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Mizuno S, Ogishima S, Nishigori H, Jamieson DG, Verspoor K, Tanaka H, Yaegashi N, Nakaya J. The Pre-Eclampsia Ontology: A Disease Ontology Representing the Domain Knowledge Specific to Pre-Eclampsia. PLoS One 2016; 11:e0162828. [PMID: 27788142 PMCID: PMC5082890 DOI: 10.1371/journal.pone.0162828] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 08/29/2016] [Indexed: 11/24/2022] Open
Abstract
Pre-eclampsia (PE) is a clinical syndrome characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation, and is a leading cause of maternal and perinatal morbidity and mortality. Previous studies have gathered abundant data about PE such as risk factors and pathological findings. However, most of these data are not semantically structured. Clinical data on PE patients are often generated with semantic heterogeneity such as using disparate terminology to describe the same phenomena. In clinical studies, interoperability of heterogenic clinical data is required in various situations. In such a situation, it is necessary to develop an interoperable and standardized semantic framework to research the pathology of PE more comprehensively and to achieve interoperability of heterogenic clinical data of PE patients. In this study, we developed an ontology representing clinical features, treatments, genetic factors, environmental factors, and other aspects of the current knowledge in the domain of PE. We call this pre-eclampsia ontology “PEO”. To achieve interoperability with other ontologies, the core structure of PEO was compliant with the hierarchy of the Basic Formal Ontology (BFO). The PEO incorporates a wide range of key concepts and terms of PE from clinical and biomedical research in structuring the knowledge base that is specific to PE; therefore, PEO is expected to enhance PE-specific information retrieval and knowledge discovery in both clinical and biomedical research fields.
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Affiliation(s)
- Satoshi Mizuno
- Department of Clinical Informatics, Tohoku University Graduate School of Medicine 2–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
- Department of Bioclinical Inforamtics, Tohoku Medical Megabank Organization, Tohoku University 2–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
- * E-mail: (SM); (SO)
| | - Soichi Ogishima
- Department of Bioclinical Inforamtics, Tohoku Medical Megabank Organization, Tohoku University 2–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
- * E-mail: (SM); (SO)
| | - Hidekazu Nishigori
- Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine 1–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | | | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia
| | - Hiroshi Tanaka
- Department of Bioclinical Inforamtics, Tohoku Medical Megabank Organization, Tohoku University 2–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Nobuo Yaegashi
- Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine 1–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
| | - Jun Nakaya
- Department of Clinical Informatics, Tohoku University Graduate School of Medicine 2–1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan
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Sahoo SS, Ramesh P, Welter E, Bukach A, Valdez J, Tatsuoka C, Bamps Y, Stoll S, Jobst BC, Sajatovic M. Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research. Int J Med Inform 2016; 94:21-30. [PMID: 27573308 PMCID: PMC5010027 DOI: 10.1016/j.ijmedinf.2016.06.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 06/15/2016] [Accepted: 06/18/2016] [Indexed: 11/18/2022]
Abstract
We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies.
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Affiliation(s)
- Satya S Sahoo
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States; Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University, Cleveland, OH 44106, United States.
| | - Priya Ramesh
- Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Elisabeth Welter
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH 44106, United States
| | - Ashley Bukach
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH 44106, United States
| | - Joshua Valdez
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Curtis Tatsuoka
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH 44106, United States
| | - Yvan Bamps
- Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Shelley Stoll
- Center for Managing Chronic Disease, University of Michigan, Ann Arbor, MI 48109, United States
| | - Barbara C Jobst
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, United States
| | - Martha Sajatovic
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH 44106, United States
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Sahoo SS, Wei A, Valdez J, Wang L, Zonjy B, Tatsuoka C, Loparo KA, Lhatoo SD. NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig. Front Neuroinform 2016; 10:18. [PMID: 27375472 PMCID: PMC4895075 DOI: 10.3389/fninf.2016.00018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 05/17/2016] [Indexed: 11/13/2022] Open
Abstract
The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.
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Affiliation(s)
- Satya S Sahoo
- Division of Medical Informatics, School of Medicine, Case Western Reserve UniversityCleveland, OH, USA; Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve UniversityCleveland, OH, USA
| | - Annan Wei
- Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University Cleveland, OH, USA
| | - Joshua Valdez
- Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Li Wang
- Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Bilal Zonjy
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
| | - Kenneth A Loparo
- Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University Cleveland, OH, USA
| | - Samden D Lhatoo
- Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA
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Ramesh P, Wei A, Welter E, Bamps Y, Stoll S, Bukach A, Sajatovic M, Sahoo SS. Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2016; 2015:731-736. [PMID: 27069752 DOI: 10.1109/bibm.2015.7359776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with "expansion of query expression" using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.
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Affiliation(s)
- Priya Ramesh
- Division of Medical Informatics, Case Western Reserve University, Cleveland, OH
| | - Annan Wei
- Division of Medical Informatics, Case Western Reserve University, Cleveland, OH
| | - Elisabeth Welter
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH
| | - Yvan Bamps
- Rollins School of Public Health, Emory University, Atlanta, GA
| | - Shelley Stoll
- Center for Managing Chronic Disease, University of Michigan, Ann Arbor, MI
| | - Ashley Bukach
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH
| | - Martha Sajatovic
- Neurological Institute, University Hospitals Case Medical Center, Cleveland, OH
| | - Satya S Sahoo
- Division of Medical Informatics, Case Western Reserve University, Cleveland, OH
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Zhang GQ, Tao S, Xing G, Mozes J, Zonjy B, Lhatoo SD, Cui L. NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research. JMIR Med Inform 2015; 3:e35. [PMID: 26554419 PMCID: PMC4704892 DOI: 10.2196/medinform.4959] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 09/20/2015] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers. OBJECTIVE This paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers. METHODS NHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f1, f2, ..., fk. The input for each function fi has 3 components: a random number r, an integer n, and input data m. The result, fi(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f1(r1, n1, m1), f2(r2, n2, m2), ..., fk(rk, nk, mk). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier. RESULTS We performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP). CONCLUSIONS The CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash.
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Affiliation(s)
- Guo-Qiang Zhang
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
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Daniel C, Choquet R. Information Technology for Clinical, Translational and Comparative Effectiveness Research. Findings from the Yearbook 2015 Section on Clinical Research Informatics. Yearb Med Inform 2015; 10:178-82. [PMID: 26293866 DOI: 10.15265/iy-2015-030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care. METHOD We provide a synopsis of the articles selected for the IMIA Yearbook 2015, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,594 articles and the evaluation results were merged for retaining 15 articles for peer-review. RESULTS The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles regarding data management and genome medicine that are mainly tool-based papers. In the first article, the authors present PPISURV a tool for uncovering the role of specific genes in cancer survival outcome. The second article describes the classifier PredictSNP which combines six performing tools for predicting disease-related mutations. In the third article, by presenting a high-coverage map of the human proteome using high resolution mass spectrometry, the authors highlight the need for using mass spectrometry to complement genome annotation. The fourth article is also related to patient survival and decision support. The authors present datamining methods of large-scale datasets of past transplants. The objective is to identify chances of survival. CONCLUSIONS The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care. Indeed, there is a need for powerful tools for managing and interpreting complex, large-scale genomic and biological datasets, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts contribute to the challenge of impacting clinically the obtained results towards a personalized medicine.
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Affiliation(s)
- C Daniel
- Christel Daniel, MD, PhD, INSERM UMRS 1142, CCS Patient - Assistance Publique - Hôpitaux de Paris, 05 rue Santerre - 75 012 PARIS, France, Tel: +33 1 48 04 20 29, E-mail:
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McCabe PH. Would Sherlock Holmes agree with our definition of rational polytherapy? A proposal for a national data bank on patients with epilepsy. Epilepsy Behav 2015; 45:147-50. [PMID: 25769675 DOI: 10.1016/j.yebeh.2015.02.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 02/06/2015] [Accepted: 02/07/2015] [Indexed: 11/18/2022]
Affiliation(s)
- Paul H McCabe
- Neurology, Epilepsy Division, Geisinger Health System, 1800 Mulberry Street, Scranton, PA 18510, USA.
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