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Total 65 results found since Jan 2013.
Cochrane ' s 30 under 30: Joel Pollet
Cochrane is made up of 13,000 members and over 50,000 supporters come from more than 130 countries, worldwide. Our volunteers and contributors are researchers, health professionals, patients, carers, people passionate about improving health outcomes for everyone, everywhere.Cochrane is an incredible community of people who all play their part in improving health and healthcare globally. We believe that by putting trusted evidence at the heart of health decisions we can achieve a world of improved health for all. Many of our contributors are young people working with Cochrane as researchers, citizen scientists...
Source: Cochrane News and Events - November 13, 2018 Category: Information Technology Authors: Katie Abbotts Source Type: news
Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network
AbstractAccording to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner...
Source: Health and Technology - January 9, 2019 Category: Information Technology Source Type: research
Cochrane ’s Neurological Sciences Field launches 2019 Summer School for young physicians and trainees interested in cerebrovascular diseases
Discussions on evidence-based medicine (EBM), elements of statistics, and what is needed to appraise evidence will be conducted so that participants will be encouraged to promote clinical EBM research and systematic reviews in their professional activity to manage uncertainty.After successful completion of the course, participants will bridge the research-practice gap in a context of evidence-based education through: knowing when and how to screen for particular conditions;having an understanding of how to appraise the evidence from trials and systematic reviewsknowing how to read a Cochrane Summary of Findings Table;unde...
Source: Cochrane News and Events - June 10, 2019 Category: Information Technology Authors: Muriah Umoquit Source Type: news
A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning
AbstractBrain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies...
Source: Journal of Medical Systems - October 22, 2019 Category: Information Technology Source Type: research
Classification and prediction of diabetes disease using machine learning paradigm
ConclusionThe combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.
Source: Health Information Science and Systems - January 2, 2020 Category: Information Technology Source Type: research
A stacked LSTM for atrial fibrillation prediction based on multivariate ECGs
AbstractAtrial fibrillation (AF) is an irregular and rapid heart rate that can increase the risk of various heart-related complications, such as the stroke and the heart failure. Electrocardiography (ECG) is widely used to monitor the health of heart disease patients. It can dramatically improve the health and the survival rate of heart disease patients by accurately predicting the AFs in an ECG. Most of the existing researches focus on the AF detection, but few of them explore the AF prediction. In this paper, we develop a recurrent neural network (RNN) composed of stacked LSTMs for AF prediction, which called SLAP. This ...
Source: Health Information Science and Systems - April 20, 2020 Category: Information Technology Source Type: research
Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network
AbstractAtrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in t...
Source: Journal of Medical Systems - May 9, 2020 Category: Information Technology Source Type: research
Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health.
Conclusions: Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.
PMID: 32547805 [PubMed]
Source: Healthcare Informatics Research - June 19, 2020 Category: Information Technology Tags: Healthc Inform Res Source Type: research
Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
CONCLUSION: The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit.
PMID: 32578538 [PubMed - in process]
Source: Studies in Health Technology and Informatics - June 26, 2020 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research
Mobile Application as a Learning Aid for Nurses and Nursing Students to Identify and Care for Stroke Patients: Pretest and Posttest Results
In this study, a total of 115 nurses from health services in the South of Brazil and 35 nursing students of a community university participated. The stages focused on development, modeling of clinical cases, problem-based learning, pretest (before) app use, and posttest (after) use of the app. The results of the pretest and posttest corrections showed a substantial statistical difference (P
Source: CIN: Computers, Informatics, Nursing - July 1, 2020 Category: Information Technology Tags: FEATURES Source Type: research
Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis
AbstractAutomatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision b...
Source: Journal of Medical Systems - August 31, 2020 Category: Information Technology Source Type: research
Featured Reviews: Behavioural activation therapy for depression
How well does behavioural activation therapy work for depression in adults? And what about the effects of this treatment on depression for adults with long‐term physical conditions? Two new Cochrane systematic reviews look at the available evidence.Depression is a common mental health problem. It can cause a persistent feeling of sadness and loss of interest in people, activities, and things that were once enjoyable. Treatments for depression include psychological therapies (talking therapies). Two reviews recently published byCochrane Common Mental Disordersfocus on a type of psychological therapy called behavioural a...
Source: Cochrane News and Events - September 9, 2020 Category: Information Technology Authors: Muriah Umoquit Source Type: news
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.PMID:33965930 | DOI:10.3233/SHTI210100
Source: Studies in Health Technology and Informatics - May 9, 2021 Category: Information Technology Authors: Michael Schrempf Diether Kramer Stefanie Jauk Sai P K Veeranki Werner Leodolter Peter P Rainer Source Type: research
