Abstract
BackgroundMedical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education.ObjectiveUsing authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public.MethodsThe selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline.ResultsUsing five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets.ConclusionsWe developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.
Highlights
BackgroundWeb-based health education and promotion has become increasingly popular among all age groups [1]
The core question of our study is to develop machine learning models to discriminate and predict what constitutes a suitable writing style of web-based health resources on children’s health promotion and education
Our study aims to assess the writing styles of web-based health resources on children’s health through an integrated, holistic approach, that is, the development of machine learning models to evaluate whether the content and the writing style of a piece of web-based health educational material is more related to children’s health promotion and education, or more for the general public
Summary
BackgroundWeb-based health education and promotion has become increasingly popular among all age groups [1]. Clinical and academic research shows that effective writing styles can have an impact on the understanding and reception of medical and health educational resources for different reader groups [4,5,6]. There is a pressing need to investigate the writing style of web-based health resources on children’s health promotion and education for the main readers of such materials as parents and child caregivers to ensure information relevance and acceptability. The Agency for Healthcare Research and Quality has developed the Patient Education Materials Assessment Tool (PEMAT) to ensure the development and delivery of quality health care products and services. Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.