7 research outputs found
Author Correction: Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features
No full textAn amendment to this paper has been published and can be accessed via a link at the top of the paper
Table_1_Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.docx
No full textEarly prediction of the occurrence of ventricular tachyarrhythmia (VTA) has a potential to save patients’ lives. VTA includes ventricular tachycardia (VT) and ventricular fibrillation (VF). Several studies have achieved promising performances in predicting VT and VF using traditional heart rate variability (HRV) features. However, as VTA is a life-threatening heart condition, its prediction performance requires further improvement. To improve the performance of predicting VF, we used the QRS complex shape features, and traditional HRV features were also used for comparison. We extracted features from 120-s-long HRV and electrocardiogram (ECG) signals (QRS complex signed area and R-peak amplitude) to predict the VF onset 30 s before its occurrence. Two artificial neural network (ANN) classifiers were trained and tested with two feature sets derived from HRV and the QRS complex shape based on a 10-fold cross-validation. The prediction accuracy estimated using 11 HRV features was 72%, while that estimated using four QRS complex shape features yielded a high prediction accuracy of 98.6%. The QRS complex shape could play a significant role in performance improvement of predicting the occurrence of VF. Thus, the results of our study can be considered by the researchers who are developing an application for an implantable cardiac defibrillator (ICD) when to begin ventricular defibrillation.</p
The Impact of COVID-19 and FAIR Data Innovation on Distance Education in Africa
No full textPrior to the advent of the COVID-19 pandemic, distance education, a mode of education that allows teaching and learning to occur beyond the walls of traditional classrooms using electronic media and online delivery practices, was not widely embraced as a credible alternative mode of delivering education, especially in Africa. In education, the pandemic, and the measures to contain it, created a need for virtual learning/teaching and showcased the potential of distance education. This article explores the potential of distance education with an emphasis on the role played by COVID-19, the technologies employed, and the benefits, as well as how data stewardship can enhance distance education. It also describes how distance education can make learning opportunities available to the less privileged, geographically displaced, dropouts, housewives, and even workers, enabling them to partake in education while being engaged in other productive aspects of life. A case study is provided on the Dutch Organisation for Internationalisation in Education (NUFFIC) Digital Innovation Skills Hub (DISH) project, which is implemented via distance education and targeted towards marginalised individuals such as refugees and displaced persons in Ethiopia, Somalia, and other conflict zones, aiming to provide them with critical and soft skills for remote work for financial remuneration. This case study shows that distance education is the way forward in education today, as it has the capability to reach millions of learners simultaneously, educating, lifting people out of poverty, and increasing productivity and yields, while ensuring that the world is a better place for future generations.</p
FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia
No full textThis paper investigates whether or not there is a policy window for making health data 'Findable', 'Accessible' (under well-defined conditions), 'Interoperable' and 'Reusable' (FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their 'FAIR Equivalency'. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and (re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.</p
Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic
No full textRapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points-distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable)-have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.</p
Incomplete COVID-19 Data:The curation of medical health data by the virus outbreak data Network-Africa
No full textThe incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally. This has become particularly clear with the recent emergence of new variants of concern. The Virus Outbreak Data Network (VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care, which renders data production largely meaningless to those producing it. This modus operandi leads to disfranchisement over the control of health data, which is extracted to be processed elsewhere. In response to this problem, VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process, would have a greater chance of being adopted. The design team based their work on the legal requirements of the European Union's General Data Protection Regulation (GDPR); the FAIR Guidelines on curating data as Findable, Accessible (under well-defined conditions), Interoperable and Reusable (FAIR); and national regulations applying in the context where the data is produced. The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data. A condition of such innovation is that the innovation team is intradisciplinary, involving stakeholders and experts from all of the places where the innovation is designed, and employs a methodology of co-creation and capacity-building.</p
Design of a FAIR digital data health infrastructure in Africa for COVID‐19 reporting and research
No full textThe limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and-through data visiting, across facilities-for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.Computer Systems, Imagery and Medi
