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_Journal of Epilepsy

Authors：

 LhatooSamden D , BernasconiNeda , BlumckeIngmar , 郭崇伦 , 慕洁　审

Corresponding author：

LhatooSamden D, Email: slhatoo@aol.com

Keywords：

大数据; 癫痫; 癫痫信息学; 癫痫本体论

DOI：

10.7507/2096-0247.20210029

Video：

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癫痫是一种具有不同病因、表型和基因型特征的异质性疾病。因此，癫痫的临床和研究方面也是多种多样的，从流行病学到分子，其范围涵盖了临床试验和结果、基因和药物发现、影像学、脑电图、病理学、癫痫外科手术和数字技术等许多领域。当前，癫痫数据以 TB 和 PB 的形式收集，突破了其能力的极限。现代计算技术在机器和深度学习方面的优势和进步已经在其他疾病中开创了先河，也为癫痫领域开辟了令人兴奋的可能性。然而，若无精心设计的方法来获取、标准化、管理和提供此类数据，则存在着失败的风险。因此，通过利益相关者的密切投入，仔细构建相关的本体，为更雄心勃勃的大数据事业（如数据共享）提供了必要的支架。在本研究中，我们评估了大数据领域的癫痫临床和研究前景、当前挑战和未来方向，并提出了癫痫大数据的系统化处理方法。

Citation： LhatooSamden D, BernasconiNeda, BlumckeIngmar, 郭崇伦, 慕洁　审. 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. Journal of Epilepsy, 2021, 7(2): 173-182. doi: 10.7507/2096-0247.20210029 Copy

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3.	Lhatoo S, Noebels J, Whittemore V. Sudden unexpected death in epilepsy: identifying risk and preventing mortality. Epilepsia, 2015, 56(10): 1700-1706.
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6.	Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc, 2018, 25: 1351-1358.
7.	Stead M, Halford JJ. Proposal for a standard format for neurophysiology data recording and exchange. J Clin Neurophysiol, 2016, 33: 403-413.
8.	Kearney H, Byrne S, Cavalleri GL, et al. Tackling epilepsy with high-definition precision medicine: a review. JAMA Neurol, 2019, 76(9): 1109.
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15.	Berg AT, Cross JH. Towards a modern classification of the epilepsies? Lancet Neurol, 2010, 9: 459–461.
16.	Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25–9.
17.	Sahoo SS, Lhatoo SD, Gupta DK, et al. Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care. J Am Med Inform Assoc, 2014, 21(1): 82-89.
18.	Cui L, Bozorgi A, Lhatoo SD, et al. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. AMIA Annu Symp Proc, 2012, 12: 1191-1200.
19.	Cui L, Huang Y, Tao S, et al. ODaCCI: ontology-guided data curation for multisite clinical research data integration in the NINDS Center for SUDEP Research. AMIA Annu Symp Proc, 2016, 16: 441-450.
20.	Cui L, Sahoo SS, Lhatoo SD, et al. Complex epilepsy phenotype extraction from narrative clinical discharge summaries. J Biomed Inform. 2014;51: 272–9.
21.	Jayapandian C, Wei A, Ramesh P, et al. A scalable neuroinformatics data flow for lectrophysiological signals using MapReduce. Front Neuroinform, 2015, 9(1): 4.
22.	Jayapandian CP, Chen CH, Bozorgi A, et al. Cloudwave: distributed processing of "big data" from electrophysiological recordings for epilepsy clinical research using Hadoop. AMIA Annu Symp Proc, 2013, 13: 691-700.
23.	Sahoo SS, Jayapandian C, Garg G, et al. Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research. J Am Med Inform Assoc. 2014;21: 263–71.
24.	Sahoo SS, Wei A, Valdez J, et al. NeuroPigPen: a scalable toolkit for processing electrophysiological signal data in neuroscience applications using Apache Pig. Front Neuroinform, 2016, 10: 18.
25.	Sahoo SS, Zhao M, Luo L, et al. OPIC: ontology-driven patient information capturing system for epilepsy. AMIA Annu Symp Proc, 2012, 12: 799-808.
26.	Zhang GQ, Cui L, Lhatoo S, et al. MEDCIS: multi-modality epilepsy data capture and integration system. AMIA Annu Symp Proc, 2014, 14: 1248-1257.
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28.	Schulze-Bonhage A, Feldwisch-Drentrup H, Ihle M. The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods. Epilepsy Behav, 2011, 22(Suppl 1): S88-93.
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1. Lohr S. The origins of "big data": an etymological detective story. New York Times. February 1, 2013.
2. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety, Technical report, META Group. 2001.
3. Lhatoo S, Noebels J, Whittemore V. Sudden unexpected death in epilepsy: identifying risk and preventing mortality. Epilepsia, 2015, 56(10): 1700-1706.
4. Boat TF, Adamson PC, Asbury C. Elements of an Integrated National Strategy to Accelerate Research and Product Development for Rare Diseases. Washington, DC: Institute of Medicine, 2010.
5. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3: 160018.
6. Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc, 2018, 25: 1351-1358.
7. Stead M, Halford JJ. Proposal for a standard format for neurophysiology data recording and exchange. J Clin Neurophysiol, 2016, 33: 403-413.
8. Kearney H, Byrne S, Cavalleri GL, et al. Tackling epilepsy with high-definition precision medicine: a review. JAMA Neurol, 2019, 76(9): 1109.
9. Gymrek M, McGuire AL, Golan D, et al. Identifying personal genomes by surname inference. Science, 2013, 339(1): 321-324.
10. Clayton EW, Evans BJ, Hazel JW, et al. The law of genetic privacy: applications, implications, and limitations. J Law Biosci. 2019;6: 1–36.
11. Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform, 2008, 1: 67-79.
12. Haendel MA, Chute CG, Robinson PN. Classification, ontology, and precision medicine. N Engl J Med, 2018, 379: 1452-1462.
13. Hitzler P, Krotszch M, Parsia B, et al. OWL 2 Web ontology language primer. W3C recommendation, 2009.
14. Berg AT, Berkovic SF, Brodie MJ, et al. Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE Commission on Classification and Terminology, 2005–2009. Epilepsia, 2010, 51(3): 676-685.
15. Berg AT, Cross JH. Towards a modern classification of the epilepsies? Lancet Neurol, 2010, 9: 459–461.
16. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25–9.
17. Sahoo SS, Lhatoo SD, Gupta DK, et al. Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care. J Am Med Inform Assoc, 2014, 21(1): 82-89.
18. Cui L, Bozorgi A, Lhatoo SD, et al. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. AMIA Annu Symp Proc, 2012, 12: 1191-1200.
19. Cui L, Huang Y, Tao S, et al. ODaCCI: ontology-guided data curation for multisite clinical research data integration in the NINDS Center for SUDEP Research. AMIA Annu Symp Proc, 2016, 16: 441-450.
20. Cui L, Sahoo SS, Lhatoo SD, et al. Complex epilepsy phenotype extraction from narrative clinical discharge summaries. J Biomed Inform. 2014;51: 272–9.
21. Jayapandian C, Wei A, Ramesh P, et al. A scalable neuroinformatics data flow for lectrophysiological signals using MapReduce. Front Neuroinform, 2015, 9(1): 4.
22. Jayapandian CP, Chen CH, Bozorgi A, et al. Cloudwave: distributed processing of "big data" from electrophysiological recordings for epilepsy clinical research using Hadoop. AMIA Annu Symp Proc, 2013, 13: 691-700.
23. Sahoo SS, Jayapandian C, Garg G, et al. Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research. J Am Med Inform Assoc. 2014;21: 263–71.
24. Sahoo SS, Wei A, Valdez J, et al. NeuroPigPen: a scalable toolkit for processing electrophysiological signal data in neuroscience applications using Apache Pig. Front Neuroinform, 2016, 10: 18.
25. Sahoo SS, Zhao M, Luo L, et al. OPIC: ontology-driven patient information capturing system for epilepsy. AMIA Annu Symp Proc, 2012, 12: 799-808.
26. Zhang GQ, Cui L, Lhatoo S, et al. MEDCIS: multi-modality epilepsy data capture and integration system. AMIA Annu Symp Proc, 2014, 14: 1248-1257.
27. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol, 2005, 116(3): 569-587.
28. Schulze-Bonhage A, Feldwisch-Drentrup H, Ihle M. The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods. Epilepsy Behav, 2011, 22(Suppl 1): S88-93.
29. Cook MJ, O'Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol, 2013, 12: 563-571.
30. Ihle M, Feldwisch-Drentrup H, Teixeira CA, et al. EPILEPSIAE—a European epilepsy database. Comput Methods Programs Biomed, 2012, 106(1): 127-138.
31. Klatt J, Feldwisch-Drentrup H, Ihle M, et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia, 2012, 53(12): 1669-1676.

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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

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