JMIR Medical Informatics
Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2015: 4.532), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.
JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).
Mar 27, 2017
Mar 22, 2017
Mar 3, 2017
Feb 24, 2017
Feb 22, 2017
Feb 17, 2017
Feb 2, 2017
Jan 17, 2017
Jan 5, 2017
Dec 22, 2016
Nov 30, 2016
Nov 25, 2016
Citing this Article
Right click to copy or hit: ctrl+c (cmd+c on mac)
Latest Submissions Open for Peer-Review:View All Open Peer Review Articles
What patients can tell us: topic analysis for social media on breast cancer
Date Submitted: Apr 5, 2017
Open Peer Review Period: Apr 6, 2017 - Jun 1, 2017
Background: Internet health forums are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are pro...
Background: Internet health forums are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such forums in order to analyse the quality of life of patients suffering from breast cancer. Objective: Our aim is to detect the different topics discussed by patients on social media and to relate them to the functional and symptomatic dimensions assessed in the internationally standardized auto-questionnaires used in cancer clinical trials (EORTC QLQ-C30 and EORTC QLQ-BR23). Methods: First, we applied a classic text mining technique, Latent Dirichlet Allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model on two data sets composed of messages extracted from public Facebook groups and from a public health forum (“cancerdusein.org”) with relevant preprocessing. Secondly, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the auto-questionnaires used to study quality of life. Results: Among the 23 topics present in the auto-questionnaires, 22 topics match with the topics discussed by patients on social media. Interestingly, these topics correspond to 95% of the forum and 86% of the Facebook groups. These figures underline that topics related to quality of life are an important concern for patients. However, 5 topics from social media did not find correspond in the auto-questionnaires which do not cover all the concerns of the patients. 2 out of these 5 topics can be used in the auto-questionnaires and these 2 topics corresponded to a total of 4.3% of topics in the “cancerdusein.org” corpus and 3.1% of the Facebook corpus. Conclusions: This work demonstrates that we have found a good correspondence between detected topics on social media and topics present in auto-questionnaires, which substantiates the sound construction of such auto-questionnaires. We detected new emerging topics from social media that can be used to complete current auto-questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of the quality of life.