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 2016: 5.175), 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).
Nov 14, 2017
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Oct 31, 2017
Oct 24, 2017
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Oct 11, 2017
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Latest Submissions Open for Peer-Review:View All Open Peer Review Articles
Potential application of connected tuberculosis diagnostics for real-time surveillance of drug resistant TB transmission
Date Submitted: Nov 1, 2017
Open Peer Review Period: Nov 2, 2017 - Dec 28, 2017
Background: Tuberculosis (TB) is the top killer infectious disease in the world, and yet the surveillance of this disease is still paper-based. Drug resistant TB is an urgent public health crisis, and...
Background: Tuberculosis (TB) is the top killer infectious disease in the world, and yet the surveillance of this disease is still paper-based. Drug resistant TB is an urgent public health crisis, and the World Health Organization has endorsed since 2010 a series of rapid diagnostic tests (RDTs) that allowed rapid detection of drug resistant strains and produced large volumes of data. In parallel, most high burden countries have adopted connectivity solutions that allow linking of diagnostics, real-time capture and shared repository of these test results. However, these connected diagnostics and readily available test results are not utilised to their full capacity as we have yet to capitalize on fully understanding the relationship between test results and specific rpoB mutations to elucidate its potential application on real-time surveillance. Objective: We aimed to validate and analyse RDT data in detail, and propose the potential use of connected diagnostics and associated test results for real-time evaluation of RR-TB transmission. Methods: From the Belgian Coordinated Collections of Microorganisms at the Institute of Tropical Medicine, 107 RR-TB strains harbouring 34 unique rpoB mutations, including 30 within the Rifampicin Resistance Determining Region, were selected. These strains were subjected to XpertMTB/RIF (Cepheid), GenoTypeMTBDRplusv2.0 (Hain LifeScience GmbH), and GenoscholarNTM+MDRTBII (Nipro), the results of which were validated against the strains’ available rpoB gene sequences. The reproducibility of the results was determined, and the probe reactions were analysed and visualised, and proposed for potential use in evaluating transmission. Results: TB diagnostic test results, particularly the RDT probe reactions detected the majority of RRDR mutations tested, although a few critical discrepancies between observed probe reactions and manufacturer claims were found. Based on published frequencies of probe reactions and RRDR mutations, we found specific probe reactions with high potential use in transmission studies namely XpertMTB/RIF probes A, Bdelayed, C, Edelayed; GenotypeMTBDRplusv2.0 WT2, WT5, WT6; and GenoscholarNTM+MDRTBII S1, S3. Additionally, inspection of probe reactions of disputed mutations may potentially resolve discordance between genotypic and phenotypic test results. Conclusions: We propose a novel approach for potential real-time detection of RR-TB transmission through fully utilizing connected TB diagnostics and shared repository of test results. To our knowledge, this is the first pragmatic and scalable work in response to the consensus of world-renowned TB experts in 2016 on the potential of diagnostic connectivity for accelerated efforts toward TB elimination. This is evidenced by the ability of our proposed approach to facilitate comparison of probe reactions between and among different RDTs used in the same setting. Integrating this proposed approach as a plug-in module to a connectivity platform will increase usefulness of connected TB diagnostics for RR-TB outbreak detection through real-time investigation of suspected RR-TB transmission cases based on epidemiological linking.
MIROR, An automated modular MRI clinical decision support system: an application in paediatric cancer diagnosis
Date Submitted: Oct 25, 2017
Open Peer Review Period: Oct 26, 2017 - Dec 21, 2017
Background: Advances in magnetic resonance imaging (MRI) and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyse relevant informat...
Background: Advances in magnetic resonance imaging (MRI) and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyse relevant information from MRI data to aid decision-making, prevent errors and enhance health care. Objective: The aim of this study was to design and develop a modular Medical Image Region of interest analysis tool and Repository (MIROR) for automatic processing, classification, evaluation and representation of advanced MRI data. Methods: The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumours in children (cohort of 48 children, with 37 malignant and 11 benign tumours). Mevislab software and Python have been used for development of MIROR. Regions of interests were drawn around benign and malignant body tumours on different diffusion parametric maps and extracted information was used to discriminate the malignant from benign tumours. Results: Using MIROR, the various histogram parameters derived for each tumour case when compared with the information in the repository, provided additional information for tumour characterization and facilitated the discrimination between benign and malignant tumours. Clinical decision support system cross validation showed high sensitivity and specificity in discriminating between these tumour groups for various histogram parameters, 100 % for kurtosis and entropy 85% and 78% respectively over all parameters. Conclusions: MIROR as a diagnostic tool and repository allowed the interpretation and analysis of MRI images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision-making. The modular-based format of the tool allowed integration of analyses which are not readily available clinically and streamlines future developments. Clinical Trial: N/A
Date Submitted: Oct 11, 2017
Open Peer Review Period: Oct 13, 2017 - Dec 8, 2017
The increasing use of social media and mobile health applications has generated new opportunities for health care consumers to share information about their health and wellbeing. Information shared t...
The increasing use of social media and mobile health applications has generated new opportunities for health care consumers to share information about their health and wellbeing. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. The aim of the present study is to explore methods for analyzing forum posts of breast cancer patients to discover the “hidden” aspects of disease management and recovery. An open source software MALLET was used to reduce the postings to categories with similar content. Qualitative analysis of the categorization and statistical analyses confirmed clinical significance of the results.