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Register a new userExtracting Appointment Spans from Medical Conversations
استخراج المواعيد يمتد من المحادثات الطبية
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يمكن استخراج المعلومات المهيكلة من المحادثات الطبية تقليل عبء الوثائق للأطباء ومساعدة المرضى الذين يتبعون مع خطة الرعاية الخاصة بهم.في هذه الورقة، نقدم مهمة جديدة لاستخراج المواعيد يمتد من المحادثات الطبية.نحن نؤيد هذه المهمة كمشكلة علامات تسلسل والتركيز على استخراج يمتد لسبب الموعد والوقت.ومع ذلك، فإن التسجيل المحادثات الطبية باهظة الثمن، وتستغرق وقتا طويلا، ويتطلب من خبرات مجال كبيرة.وبالتالي، نقترح أن نستفيد مناهج الإشراف الضعيفة، وهي الإشراف غير المكتملة والإشراف غير الدقيق، ونهج إشراف هجين وتقييم كل من ELMO - ELMO وبرت خاصة بالمجال باستخدام نماذج علامات التسلسل.أفضل نموذج أداء هو متغير Bertiant الخاص بالمجال باستخدام الإشراف الهجين الضعيف والحصول على درجة F1 79.32.
Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
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This paper presents a reference study of available algorithms for plagiarism
detection and it develops semantic plagiarism detection algorithm for plagiarism detection
in medical research papers by employing the Medical Ontologies available on the
World
Wide Web.
The issue of plagiarism detection in medical research written in natural languages is
a complex issue and related exact domain of medical research.
There are many used algorithms for plagiarism detection in natural language, which
are generally divided into two main categories, the first one is comparison algorithms
between files by using fingerprints of files, and files content comparison algorithms, which
include strings matching algorithms and text and tree matching algorithms.
Recently a lot of research in the field of semantic plagiarism detection algorithms
and semantic plagiarism detection algorithms were developed basing of citation analysis
models in scientific research.
In this research a system for plagiarism detection was developed using “Bing” search
engine, where tow type of ontologies used in this system, public ontology as wordNet and
many standard international ontologies in medical domain as Diseases ontology which
contains a descriptions about diseases and definitions of it and the derivation between
diseases.
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