An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums
المؤلفون المشاركون
Gao, Jun
Liu, Ninghao
Lawley, Mark
Hu, Xia
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-03
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Online healthcare forums (OHFs) have become increasingly popular for patients to share their health-related experiences.
The healthcare-related texts posted in OHFs could help doctors and patients better understand specific diseases and the situations of other patients.
To extract the meaning of a post, a commonly used way is to classify the sentences into several predefined categories of different semantics.
However, the unstructured form of online posts brings challenges to existing classification algorithms.
In addition, though many sophisticated classification models such as deep neural networks may have good predictive power, it is hard to interpret the models and the prediction results, which is, however, critical in healthcare applications.
To tackle the challenges above, we propose an effective and interpretable OHF post classification framework.
Specifically, we classify sentences into three classes: medication, symptom, and background.
Each sentence is projected into an interpretable feature space consisting of labeled sequential patterns, UMLS semantic types, and other heuristic features.
A forest-based model is developed for categorizing OHF posts.
An interpretation method is also developed, where the decision rules can be explicitly extracted to gain an insight of useful information in texts.
Experimental results on real-world OHF data demonstrate the effectiveness of our proposed computational framework.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Gao, Jun& Liu, Ninghao& Lawley, Mark& Hu, Xia. 2017. An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1180857
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Gao, Jun…[et al.]. An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums. Journal of Healthcare Engineering No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1180857
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Gao, Jun& Liu, Ninghao& Lawley, Mark& Hu, Xia. An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1180857
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1180857
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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