دانلود مقاله انگلیسی رایگان:پیش بینی نتایج درمان دارویی ضد صرع بیماران مبتلا به صرع تازه کشف شده با یادگیری ماشین - 2019
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  • Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning
    Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning


    ترجمه فارسی عنوان مقاله:

    پیش بینی نتایج درمان دارویی ضد صرع بیماران مبتلا به صرع تازه کشف شده با یادگیری ماشین


    منبع:

    Sciencedirect - Elsevier - Epilepsy & Behavior 96 (2019) 92–97


    نویسنده:

    Lijun Yao a,⁎, Mengting Cai b, Yang Chen c, Chunhong Shenb, Lei Shi d, Yi Guob,⁎


    چکیده انگلیسی:

    Objective: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. Methods: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features,medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. Results: Our study shows that 1) comparedwith the other four algorithms, the XGBoost algorithmbased machine learning model achieves the best prediction performance of the AEDtreatment outcomes between remission and never remission patientswith an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (N3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. Significances: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifiers prediction result could help disease guide counseling and eventually improve treatment strategies
    Keywords: Machine learning | Epilepsy | Epilepsy remission | Antiepileptic drug | Outcome prediction


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 393 کیلوبایت

    قیمت: رایگان


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