Applied Bimatics - An Informatics & eHealth Blog

I am a clinician with a passion for informatics. This blog is about my eHealth journey exploring interoperability in Electronic Medical Records (EMR/EHR), Patient Safety, Pharmacovigilance, Data Analytics, Clinical Research and Bioinformatics in a clinical context. Comparing Canadian, Indian and Middle Eastern healthcare systems and services. Join our open facebook group here: https://www.facebook.com/groups/clinical.bioinformaticians/


A Systematic Review for Mobile Monitoring Solutions in M-Health

Abstract

A systematic review allows us to identify, assess, and interpret all possible relevant work associated with a question in particular or the subject of an area. Different authors can use several methodologies to learn about research related to their own research in different fields. The main objective of this review is to identify work, research and publications made in the field of the mobile monitoring of patients through some application or commercial or non-commercial solutions in m-Health. Next, we compare the different solutions with our solution, MoMo (Mobile Monitoring) Framework. MoMo is a solution that allows for patient mobile monitoring through mobile phones and biometric devices (blood pressure meter, glucometer and others). Our systematic review is based on the methodology of B. Kitchenham. She proposed specific guidelines for carrying out a systematic review in software engineering. We prepare our systematic review base in the selection of primary and secondary research related to mobile monitoring solutions following criteria with a specific weight to compare with each part of our research.



By

Read More....

Labels: ,


commentAdd Your Comments/Suggestions/Criticisms! | View Comments | Links to this post


A model for comparative analysis of medical bone X-ray images using image segmentation

Image enhancement is the pre-processing stage of the image processing. The objective is to improve the visual effects and the perception of knowledge in images for viewers and to provide a better input for automated image processing technique. It gives emphasis on the whole or part features of the graphics in the designated image applications to enlarge the objects in the graphics. The enhanced image by use of threshold segmentation method identifies bone fracture in medical X-ray images. In this research work, the image is taken as input and after noise removal, the X-ray image of right hand and hairline bone fracture image is being segmented using simple thresholding, multiple thresholding and optimal thresholding method and compared with each other so as to choose the best technique for threshold image segmentation.

By Barnali Sahu; Shweta Jena; Alok Kumar Jagadev in International Journal of Telemedicine and Clinical Practices (IJTMCP), 2016 Vol. 1 No. 3, pp. 199 - 208

Read More....

Labels: ,


commentAdd Your Comments/Suggestions/Criticisms! | View Comments | Links to this post


DATA model for a multidimensional decision making in healthcare

Health technology assessment (HTA) is increasingly used in European countries to inform decision and policy making in the healthcare sector. Several countries have integrated HTA into policy, governance or regulatory processes. The present research aims to propose a new multidisciplinary approach to support and to justify decisions policies in healthcare organisations for the standardisation of HTA information and for the achievement of a more quality decisions under uncertainty, which ultimately determines the success of organisations. A multidimensional model, called DATA model - decision analytic technology assessment model - based on analytic hierarchy process and the Cyert-March-Simon model (aka Carnegie decision model) is developed. The results obtained show the potentiality of the proposed approach in prioritising critical aspects and in supporting management performance quality in healthcare system.

By Antonella Petrillo; Fabio De Felice; Laura Petrillo in International Journal of Multicriteria Decision Making (IJMCDM), 2016 Vol. 6 No. 2, pp. 138 - 156

Read More....

Labels: ,


commentAdd Your Comments/Suggestions/Criticisms! | View Comments | Links to this post


Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis

The primary contribution of this study relies on proposing a new method, which can detect heart diseases in respective heart valve data. In this work, supervised quick reduct feature selection algorithm is applied for selecting important features from heart valve data. The classification method is applied only for relevant features selected using supervised quick reduct from heart valve data. In this paper, a new classification approach based on pessimistic multi-granulation rough sets (PMGRS) is applied for heart valve disease diagnosis. In multi-granulation rough sets, set approximations are well-defined by multiple equivalence relations on the universe, leading to an effective model for classification. This is confirmed by experimental evaluation, which shows excellent classification performance and also demonstrates that the proposed approach is superior to other benchmark classification algorithms including naïve Bayes, multi-layer perceptron (MLP), and J48 and decision table classifiers.

By Ahmad Taher Azar; S. Senthil Kumar; H. Hannah Inbarani; Aboul Ella Hassanien in International Journal of Modelling, Identification and Control (IJMIC), 2016 Vol. 26 No. 1, pp. 42 - 51

Read More....

Labels: ,


commentAdd Your Comments/Suggestions/Criticisms! | View Comments | Links to this post


Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application

• We propose a method of combining several predictors into a new one that will perform at the level of the best involved one, or even outperform all individual candidates.• Portability of the method. This feature of the method allows its simple implementation in the form of a diabetic Smartphone app.• The potential for everyday use by any patient who performs self-monitoring of blood glucose.• The idea is based on the linear functional strategy for regularized ranking.

By Pavlo Tkachenko, Galyna Kriukova, Marharyta Aleksandrova, Oleg Chertov, Eric Renard, Sergei V. Pereverzyev

Read More....

Labels: ,


commentAdd Your Comments/Suggestions/Criticisms! | View Comments | Links to this post

About Me

As a Dermatologist and Informatician my research mainly involves application of bioinformatics techniques and tools in dermatological conditions. However my research interests are varied and I have publications in areas ranging from artificial intelligence, sequence analysis, systems biology, ontology development, microarray analysis, immunology, computational biology and clinical dermatology. I am also interested in eHealth, Health Informatics and Health Policy.

Address

Bell Raj Eapen
Hamilton, ON
Canada