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sent2span: اكتشاف span لاستخراج بيكو في النص الطبي الطبيعي دون التوضيحات
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إن النمو السريع في التجارب السريرية المنشورة يجعل من الصعب الحفاظ على مراجعات منهجية محدثة، والتي تتطلب إيجاد جميع التجارب ذات الصلة. وهذا يؤدي إلى اتخاذ القرارات السياسية والممارسة بناء على مجموعة فرعية خارجية وغير كاملة منحازة من الأدلة السريرية المتاحة. قد يكون استخراج ثم تطبيع السكان والتدخل والمقارنة والمقارنة والنتيجة (PICO) من مقالات التجريبية السريرية طريقة فعالة لتعيين التجارب التلقائية تلقائيا لمراجعات منهجية وتجنب البحث والفحص --- أكثر عمليات المراجعة المنهجية التي تستغرق وقتا طويلا. نقترح واختبار نهج رواية للكشف عن SPAN. الفرق الرئيسي بين أسلوبنا المقترح والنهج السابقة يأتي من اكتشاف المواقف دون الحاجة إلى بيانات تمدد المشروح واستخدام التعليقات التوضيحية على مستوى الجملة الجماعي فقط. تظهر التجارب على مجموعة بيانات اثنين من نتائج اكتشاف PICO Span تحقيق نتائج أعلى بكثير للتذكر عند مقارنتها بالأساليب الإشرافية بالكامل مع اكتشاف جملة بيكو على الأقل جيدة مثل التوضيح البشرية. من خلال إزالة الاعتماد على التعليقات التوضيحية للخبراء للكشف عن SPAN، يمكن استخدام هذا العمل في خط أنابيب رشاشيا لتحويل التوضيحات بيكو ذات الجودة المنخفضة ذات المستوى الخشفي والجمدية إلى معلومات منظمة يمكن استخدامها بسرعة لتعيين التجارب بشكل منهجي مراجعات.
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which require finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening---the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in a human-machine pipeline for turning low-quality, crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.
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