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Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields
المؤلفون المشاركون
Dai, Hong-Jie
Syed-Abdul, Shabbir
Chen, Chih-Wei
Wu, Chieh-Chen
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-08-26
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population.
To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs.
Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information.
In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents.
In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model.
A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches.
The results of the experiments showed that the proposed method achieved a satisfactory F-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively.
One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Dai, Hong-Jie& Syed-Abdul, Shabbir& Chen, Chih-Wei& Wu, Chieh-Chen. 2015. Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields. BioMed Research International،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057095
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Dai, Hong-Jie…[et al.]. Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields. BioMed Research International No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1057095
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Dai, Hong-Jie& Syed-Abdul, Shabbir& Chen, Chih-Wei& Wu, Chieh-Chen. Recognition and Evaluation of Clinical Section Headings in Clinical Documents Using Token-Based Formulation with Conditional Random Fields. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057095
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1057095
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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