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Register a new userTowards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
نحو أتمتة التوصيلات الطبية: عيادة زيارة الحوار 23Note عقوبة المحاذاة
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يتم تلخيص المحادثات الطبية من زيارات المريض بشكل روتيني إلى الملاحظات السريرية لتوثيق الرعاية السريرية.يعد الإبداع التلقائي للملاحظة السريرية أمرا صعبا بشكل خاص بالنظر إلى أنه يتطلب التلخصات على اللغة المنطوقة وتحويلات المتكلم المتعددة؛كذلك، تشمل الملاحظات السريرية نصا شبه مهيكل للغاية.في هذه الورقة، نصف طريقة إنشاء Corpus الخاصة بنا وأنظمة الأساس لاثنين من مهام NLP، الحوار السريري 23Note محاذاة الجملة والحوار الإكلينيكي 2NOTE FILIPET.قد يتم دمج هذين النموذجين، وكذلك النماذج الأخرى التي تم إنشاؤها من مثل هذه الكائنات، كأجزاء من نظام توليد الأطراف السريري الطرفية نهاية إلى نهاية.
Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.
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We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts. We believe this approach leads to a more objective and easier to reproduce evaluation. We apply this to the task of medical report summarisation, where measuring objective quality and accuracy is of paramount importance.
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Measuring the similarity score between a pair of sentences in different languages is the essential requisite for multilingual sentence embedding methods. Predicting the similarity score consists of two sub-tasks, which are monolingual similarity eval
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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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