Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex

Author:

Zhao Xia,Jiang Dian,Hu Zhanqi,Yang Jun,Liang Dong,Yuan Bixia,Lin Rongbo,Wang Haifeng,Liao Jianxiang,Zhao CaileiORCID

Publisher

Elsevier BV

Subject

Neurology (clinical),Neurology

Reference34 articles.

1. Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data;An;Epilepsy Behav.,2018

2. Breiman, L., Classification and regression trees. The Wadsworth statistics/probability series. Wadsworth International Group; 1984:x, 358 p.

3. Seizure prediction in genetic rat models of absence epilepsy: improved performance through multiple-site cortico-thalamic recordings combined with machine learning;Budde;eNeuro,2021

4. Treatment Outcomes in Patients With Newly Diagnosed Epilepsy Treated With Established and New Antiepileptic Drugs: A 30-Year Longitudinal Cohort Study (vol 75, pg 279, year 2017);Chen;Jama Neurol.,2018

5. Big data analysis and artificial intelligence (AI) in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting;Chung;Clin. Exp. Pedia,2021

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