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- W3165713689 abstract "Abstract Although antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline." @default.
- W3165713689 created "2021-06-07" @default.
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- W3165713689 date "2021-01-11" @default.
- W3165713689 modified "2023-10-06" @default.
- W3165713689 title "Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients" @default.
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- W3165713689 doi "https://doi.org/10.1186/s42494-020-00035-9" @default.
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