JMIR Medical Informatics
Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.
JMIR Medical Informatics (JMI, ISSN 2291-9694) focusses 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 2013: 4.7), JMIR Med Inform has a 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 18, 2015
Nov 10, 2015
Oct 23, 2015
Oct 9, 2015
Sep 21, 2015
Sep 18, 2015
Sep 1, 2015
Aug 31, 2015
Jul 31, 2015
Jul 10, 2015
Jul 2, 2015
Jun 10, 2015
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Latest Submissions Open for Peer-Review:View All Open Peer Review Articles
Computer-aided Detection of Rib Fracture using Morphological Features in Chest Radiographs
Date Submitted: Nov 23, 2015
Open Peer Review Period: Nov 24, 2015 - Jan 19, 2016
Background: The detection of rib fractures is extremely important for detecting the associated injuries, preventing complications, obviating medico-legal issues, detecting pathologic fractures, and he...
Background: The detection of rib fractures is extremely important for detecting the associated injuries, preventing complications, obviating medico-legal issues, detecting pathologic fractures, and helping manage patients’ pain. However, the accuracy of detecting rib fractures from chest radiographs varies depending on the observer’s training level, the quality of the displayed images, and the clinical scenario for which the chest radiographs are obtained. Objective: We propose a new method for the detection of a rib fracture using image processing technique and morphological features in chest radiographs. Methods: The proposed method consists of the following steps: 1) acquisition of the cortical bone using an image processing technique in the region of interest (ROI); 2) acquisition of three morphological features, namely the cortical length, cortical perimeter, and cortical angle of the cortical bone region, for the fracture classification; and 3) classification of the fracture using support vector machine (SVM) classifier. Results: A statistically significant difference was found between the results of non-fracture and fracture states with respect to the defined features in the cortical bone region: cortical length (P< .001), cortical perimeter (P< .001), and cortical angle (P< .036). The result of the fracture classification using an SVM classifier revealed that the accuracy of 74.74% facilitates the classification of fractures. Conclusions: The proposed method, which includes an image processing technique for the cortical bone of ribs and the abovementioned features, could identify a fracture of the ribs from chest radiographs.
Challenges and Opportunities of Big Data in Healthcar
Date Submitted: Nov 19, 2015
Open Peer Review Period: Nov 19, 2015 - Jan 14, 2016
Background: Big data analytics offer promise in many sectors, but with the aging of society, healthcare is looking at big data to provide answers to age-related issues, particularly dementia and disea...
Background: Big data analytics offer promise in many sectors, but with the aging of society, healthcare is looking at big data to provide answers to age-related issues, particularly dementia and disease management. Objective: The purpose of this review is to summarize the challenges faced by big data analytics and the opportunities that big data opens. Methods: Four searches were performed for publications between January 1, 2010 to April 1, 2015 and an assessment made on their content germane to healthcare. From these publications (n=28), the authors summarized content and identified 9 and 11 themes under the categories Challenges and Opportunities, respectively. Results: The top challenges were issues of data structure, security, data standardisation, storage and transfers, and data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction. Conclusions: Big data analytic tools must overcome some legitimate obstacles, but the promise of its results could have positive, global implications. Clinical Trial: non applicable