Introduction And Goal: Currently, 1-2% of the population in developed countries are under treatment with oral anticoagulants. An appropriate strategy to deal with this increase in demand of treatment with oral anticoagulants and to manage the costs is the transfer of part or all of the responsibility for managing treatment to the patients. The use of information technology, particularly electronic health software, can be an appropriate method to improve the quality of self-management of treatment with these drugs. Therefore, this systematic review investigated studies that discuss the characteristics of electronic health software in self-management of oral anticoagulation therapy.
Method: A systematic review based on PRISMA protocol was conducted. In this study, articles were investigated that were in English. Articles existing in Cochrane, EMBASE and PubMed databases were searched up to 14 May 2017. Then, articles searched through Google Scholar were added to this study.
Findings: The common characteristics used in most software included 'encryption in exchanging information', having an 'instruction module' and 'being Android-based'. In terms of functionality, 'communication between the patient and healthcare team' existed in most of the software.
Conclusion: The results of the study showed that the accuracy of administration of the dose of the drug using computer to reach a target international normalized ratio level was not less than those administered with experienced medical staff. In addition, the results indicated that important characteristics of the software include encryption in exchanging information, instruction module and Android-based instruction module. The most important characteristic was the interaction between the patient and the healthcare team.
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http://dx.doi.org/10.1177/1474515119843739 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Sci Rep
January 2025
Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Software Convergence, Seoul Women's University, Hwarango 621, Nowongu, Seoul, 01797, Republic of Korea.
In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix.
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