Literature DB >> 25000012

Automatically Assessing the Expert Degree of Online Health Content using SVMs.

Richard Zowalla1, Martin Wiesner1, Daniel Pfeifer1.   

Abstract

More and more people search for health information regarding diseases, diagnoses and treatments over the Web. However, lay people often have difficulties in assessing the understandability of related articles. Therefore, they could benefit from a system, which computes the medical expert degree of a corresponding piece of text in advance. In this paper we present an approach to automatically compute this expert degree using a machine learning approach. For evaluation purposes we constructed a large text corpus and tested our trained text classifier, which is based on Support Vector Machines.

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Year:  2014        PMID: 25000012

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  4 in total

1.  Computer-Based Readability Testing of Information Booklets for German Cancer Patients.

Authors:  Christian Keinki; Richard Zowalla; Monika Pobiruchin; Jutta Huebner; Martin Wiesner
Journal:  J Cancer Educ       Date:  2019-08       Impact factor: 2.037

2.  Understandability of Patient Information Booklets for Patients with Cancer.

Authors:  Christian Keinki; Richard Zowalla; Martin Wiesner; Marie Jolin Koester; Jutta Huebner
Journal:  J Cancer Educ       Date:  2018-06       Impact factor: 2.037

3.  Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis.

Authors:  Jelizaveta Gordejeva; Richard Zowalla; Monika Pobiruchin; Martin Wiesner
Journal:  J Med Internet Res       Date:  2022-05-16       Impact factor: 7.076

4.  Crawling the German Health Web: Exploratory Study and Graph Analysis.

Authors:  Richard Zowalla; Thomas Wetter; Daniel Pfeifer
Journal:  J Med Internet Res       Date:  2020-07-24       Impact factor: 5.428

  4 in total

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