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Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients
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作者 Shijun Yang Bin Wang Xiong Han 《Acta Epileptologica》 2021年第1期1-6,共6页
Although anteplleptlc drugs(AEDs)are the most effective treatment for epllepsy,30-40%of patlents with epllepsy would develop drug-efacory eplepsy.An accurate,prellminary predlctlon of the efflcacy of AEDs has great cl... Although anteplleptlc drugs(AEDs)are the most effective treatment for epllepsy,30-40%of patlents with epllepsy would develop drug-efacory eplepsy.An accurate,prellminary predlctlon of the efflcacy of AEDs has great clinical signflcance for patent treatment and prognosts.Some studles have developed statstical models and machine-learning algorithms(MLAS)to predlct the fficacy of AEDs treatment and the progression of disease ater treatment withdrawal,In order to provlde asstance for makng cInlcal decslons In the alm of precse,personalzed treatment The fleld of predcton models with statstical models and MLAs's atracting growing Interest and's developing rapldly.What's more,more and more studles focus on the external valldation of the exlsting model In this revlew,we will glve a brlef overvlew of recent developments In this discipline. 展开更多
关键词 Prediction model Machine learning Antiepileptic drugs Drug response withdrawal reaction
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