Changing the approach to treatment choice in epilepsy using big data

Devinsky, Orrin; Dilley, Cynthia; Ozery-Flato, Michal; Aharonov, Ranit; Goldschmidt, Yaara; Rosen-Zvi, Michal; Clark, C.; Fritz, Patty

doi:10.1016/j.yebeh.2015.12.039

Cited by 58 publications

(59 citation statements)

References 21 publications

(18 reference statements)

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“…Data mining (data‐driven hypothesis generation) can reveal insights about diseases that might be otherwise undetectable, as some patterns are revealed only when viewed in aggregate. Data‐mining techniques have been applied to large databases for several neurologic conditions, including epilepsy . The Seizure Tracker database represents a rare opportunity to evaluate an extensive range of ages and etiologies, including uncommon forms of epilepsy.…”

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confidence: 99%

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Characteristics of large patient‐reported outcomes: Where can one million seizures get us?

et al. 2018

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SummaryObjectiveTo analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.MethodsZero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively.ResultsA total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004).SignificanceThis study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

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confidence: 99%

“…Data-mining techniques have been applied to large databases for several neurologic conditions, including epilepsy. 3,4 The Seizure Tracker database represents a rare opportunity to evaluate an extensive range of ages and etiologies, including uncommon forms of epilepsy.…”

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confidence: 99%

Characteristics of large patient‐reported outcomes: Where can one million seizures get us?

et al. 2018

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“…In a testing set of 8292 patients, the classifier's predicted regimens, which yielded an AUC of 72%, might have resulted in 281.5 fewer hospitalization days and fewer physician visits per year if prescribed at the time of the prediction, although only matched the prescribed regimen in 13% of cases. 89 Another study examining six EEG features before and after a medication change in 20 pediatric epilepsy patients achieved 85.71% sensitivity and 76.92% specificity in predicting subsequent treatment response (ie, >50% reduction in seizure frequency) using an SVM classifier. 90 In a similar study, clinical data (eg, age at onset, seizure frequency, family history, and abnormal imaging) were combined with EEG features (sample entropy of the α, β, δ, and θ frequency bands) from 36 newly diagnosed patients started on levetiracetam monotherapy to predict Engel I outcomes, achieving a sensitivity of 100% and specificity of 80.0% (AUC 96%) using an SVM classifier.…”

Section: Management Of Epilepsymentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

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“…We still have limited understanding of the impact of everyday events and circumstances on seizure control in the epilepsies. Environmental and other influences undoubtedly exist, but are difficult to determine and measure, since digitization of this information on a widespread scale is still missing . The development of digital datasets offers the possibility of progress in this key area through the:…”

Section: Epixchange: Recommendations For the Futurementioning

confidence: 99%

Advancing research toward faster diagnosis, better treatment, and end of stigma in epilepsy

et al. 2019

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Seven large European Union (EU)–funded epilepsy‐related research projects joined forces in May 2018 in Brussels, Belgium, in a unique community building event—the epiXchange conference. During this conference, 170 investigators from the projects DESIRE, EpimiRNA, EPISTOP, EpiTarget, EpiXchange, and EpiPGX as well as the European Reference Network EpiCARE, met up with key stakeholders including representatives of the European Commission, patient organizations, commercial partners, and other European and International groups. The epiXchange conference focused on sharing and reviewing the advances made by each project in the previous 5 years; describing the infrastructures generated; and discussing the innovations and commercial applications across five thematic areas: biomarkers, genetics, therapeutics, comorbidities, and biobanks and resources. These projects have, in fact, generated major breakthroughs including the discovery of biofluid‐based molecules for diagnosis, elucidating new genetic causes of epilepsy, creating advanced new models of epilepsy, and the pre‐clinical development of novel compounds. Workshop‐style discussions focused on how to overcome scientific and clinical challenges for accelerating translation of research outcomes and how to increase synergies between the projects and stakeholders at a European level. The resulting advances would lead toward a measurable impact of epilepsy research through better diagnostics, treatments, and quality‐of‐life for persons with epilepsy. In addition, epiXchange provided a unique forum for examining how the different projects could build momentum for future novel groundbreaking epilepsy research in Europe and beyond. This report includes the main recommendations that resulted from these discussions.

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Changing the approach to treatment choice in epilepsy using big data

Cited by 58 publications

References 21 publications

Characteristics of large patient‐reported outcomes: Where can one million seizures get us?

Characteristics of large patient‐reported outcomes: Where can one million seizures get us?

Machine learning applications in epilepsy

Advancing research toward faster diagnosis, better treatment, and end of stigma in epilepsy

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