TY - JOUR
T1 - Big data in epilepsy
T2 - Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy
AU - Lhatoo, Samden D.
AU - Bernasconi, Neda
AU - Blumcke, Ingmar
AU - Braun, Kees
AU - Buchhalter, Jeffrey
AU - Denaxas, Spiros
AU - Galanopoulou, Aristea
AU - Josephson, Colin
AU - Kobow, Katja
AU - Lowenstein, Daniel
AU - Ryvlin, Philippe
AU - Schulze-Bonhage, Andreas
AU - Sahoo, Satya S.
AU - Thom, Maria
AU - Thurman, David
AU - Worrell, Greg
AU - Zhang, Guo Qiang
AU - Wiebe, Samuel
N1 - Publisher Copyright:
© 2020 International League Against Epilepsy
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - big data
KW - epilepsy
KW - epilepsy informatics
KW - epilepsy ontology
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U2 - 10.1111/epi.16633
DO - 10.1111/epi.16633
M3 - Article
C2 - 32767763
AN - SCOPUS:85089094474
SN - 0013-9580
VL - 61
SP - 1869
EP - 1883
JO - Epilepsia
JF - Epilepsia
IS - 9
ER -