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.

中文翻译：

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癫痫大数据：临床和研究考虑。来自国际抗癫痫联盟癫痫大数据工作组的报告

癫痫是一种异质性疾病，具有不同的病因、表型和基因型特征。临床和研究方面相应地多种多样，从流行病学到分子，跨越临床试验和结果、基因和药物发现、成像、脑电图、病理学、癫痫手术、数字技术等等。癫痫数据以 TB 和 PB 的形式收集，推动了当前能力的极限。现代计算能力以及机器和深度学习的进步，在其他疾病中处于领先地位，也为癫痫病开辟了令人兴奋的可能性。然而，如果没有精心设计的方法来获取、标准化、管理和提供此类数据，就会存在失败的风险。因此，通过密切的利益相关者输入，仔细构建相关本体，为更雄心勃勃的大数据事业提供必要的脚手架，例如癫痫数据共享。在这篇综述中，我们评估了大数据领域的临床和研究癫痫状况、当前挑战和未来方向，并为癫痫大数据的系统方法提供了理由。