Abstract
BackgroundSelf-management is crucial to diabetes care and providing expert-vetted content for answering patients’ questions is crucial in facilitating patient self-management.ObjectiveThe aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients.MethodsWe compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents.ResultsWe retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively.ConclusionsThis study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients’ questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums.
Highlights
Diabetes is a chronic metabolic disease currently affecting almost 415 million patients worldwide with an estimation of this reaching 642 million by the year 2040 [1]
The Unified Medical Language System (UMLS) mapping rate for patient educational materials was significantly higher than questions from diabetic patients (P
The unique word patient educational materials were present in questions from count in questions from diabetic patients was 41,820 with 8952 diabetic patients with 25.06% (2244/8952) of the words not in in patient educational materials
Summary
Diabetes is a chronic metabolic disease currently affecting almost 415 million patients worldwide with an estimation of this reaching 642 million by the year 2040 [1]. There is an abundant supply of expert-vetted patient education resources that aim to help diabetic patients improve their diabetes self-management [7,8,9]; it is quite challenging for patients without a medical background to find relevant educational materials. A system that can automatically recommend such resources to patients based on their questions in an online forum would be one way to provide relevant expert-vetted education materials. Self-management is crucial to diabetes care and providing expert-vetted content for answering patients’ questions is crucial in facilitating patient self-management. Objective: The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. Conclusions: This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients’ questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums
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.