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Register a new userEnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways
Ensidnet: شبكة Siamese الهجينة المحسنة للتجميع التجارب السريرية لتجميع مسارات تطوير المخدرات
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تم استخدام الشبكات العصبية السيامية على نطاق واسع لأداء تصنيف التشابه في إعدادات متعددة الطبقات.يمكن استخدام بنيةهم لجماعة التجارب السريرية التي تنتمي إلى نفس مسار تنمية المخدرات على طول المراحل المحاكمة السريرية العديدة.هنا نقدم نهجا للحاجة غير الملباة لإعادة إعمار مسار تنمية المخدرات، استنادا إلى شبكة عميقة محسنة في العمل العصبي الهجين (Ensidnet).يوضح النموذج المقترح تحسنا كبيرا فوق خط الأساس في إعداد تقييم طلقة واحدة وفي إعداد التشابه الكلاسيكي.يمكن أن تكون Ensidnet أداة أساسية في بيئة تعليمية شبه إشراف: من خلال تحديد التجارب السريرية من المرجح أن تنتمي إلى نفس مسار تنمية المخدرات، من الممكن تسريع عملية وضع العلامات للخبراء البشريين، مما يسمح بفحص حجم متسقالبيانات، تستخدم كذلك في مجموعة بيانات التدريب.
Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). The proposed model demonstrates significant improvement above baselines in a 1-shot evaluation setting and in a classical similarity setting. EnSidNet can be an essential tool in a semi-supervised learning environment: by selecting clinical trials highly likely to belong to the same drug-development pathway it is possible to speed up the labelling process of human experts, allowing the check of a consistent volume of data, further used in the model's training dataset.
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