Published on Cambridge, MA: Academic Press; 2019:89-109. Among the included studies, most models for predicting asthma development had less than 80% accuracy. [. With a streamlined publishing process, flexible funding options, and more,
researchers can freely and immediately share their peer-reviewed research with the world. the productivity and citation impact of the publications of a Marcolino, Hebatullah Mohamed Although the research question focused on the impact of big data analytics on peoples health, studies assessing the impact on clinical outcomes are still scarce. To improve quality of care by improving efficient health outcomes, reducing the waste of resources, increasing productivity and performance, promoting risk reduction, and optimizing process management. Payment of the APC fee (directly to the publisher) by the author or a funding body is not required untilAFTERthe manuscript has gone through
the full double-blind peer review process and the Editor(s)-in-Chief at his/her/their full discretion has/have decided to accept the manuscript based
on the results of the double-blind peer review process. [, Alonso SG, de la Torre-Dez I, Hamrioui S, Lpez-Coronado M, Barreno DC, Nozaleda LM, et al. Evolution of the number of published documents. The artifacts made availablemustbe sufficient to ensure that published results can be accurately reproduced. Having released several books and a number of other research publications with IGI Global, I am more than happy to continue this collaboration in a friendly environment created by IGI Global staff. In: Dey N, Ashour AS, Bhat C, Fong SJ, editors. URL: Whitlock EP, Lin JS, Chou R, Shekelle P, Robinson KA. Although the study did not define the best methodology to evaluate and detect potential cases, the authors noted an elevated frequency of decision tree models, nave Bayes classifiers, and SVM algorithms used during previous pandemics. There are also other factors such as H-Index, Self-Citation Ratio, SJR, SNIP, etc. PLoS One 2020;15(6):e0234722 [, Murray NM, Unberath M, Hager GD, Hui FK. [, Shatte ABR, Hutchinson DM, Teague SJ. Peer-reviewed publications categorized as systematic reviews assessing the effects of big data analytics on any of the GPW13 and EPW health indicators and core priorities were included, regardless of language and study design. Clinical research and clinical trials significantly contribute to understanding the patterns and characteristics of diseases, as well as for improving detection of acute or chronic pathologies and to guide the development of novel medical interventions [47]. However, experimental/theoretical investigations, mathematical approaches, and computer-based studies hinge on handling sample size limitations and performing data imputation [48,49]. The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. Two reviews reported the application of big data analytics and ML to better understand the current novel coronavirus pandemic [35,37]. One review also assessed multiple sclerosis diagnosis. Different evaluation measures such as accuracy, area under the receiver operating characteristic curve, precision, recall, and F-measure capture different aspects of the task and are influenced by data characteristics such as skewness (ie, imbalance), sampling bias, etc. Similarly, many reviews were related to people requiring interventions against noncommunicable diseases. The included reviews in this study addressed many necessary health-related tasks; however, the quality of evidence was found to be low to moderate, and studies assessing the impact on clinical outcomes are notably scarce. Data standardization: concerns with limited interoperability, data obtention, mining, and sharing, along with language barriers, 4. Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. One listed the impact of continuous pharmacological exposure of pregnant women, emphasizing that AI could improve popular understanding of drug effects on pregnancy, mainly through: (i) reliable clinical information disclosure, (ii) adequate scientific research design, and (iii) implementation of DSS [30]. Publishing with IGI Global has allowed me to contribute new work across my diverse research interestsI appreciate the IGI Global mission to disrupt the scholarly publishing community with its systematic process for expert peer review and inter/transdisciplinary subject areas. Studies with a review protocol tracking number were analyzed. (2021). Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models.
[, GBD 2016 Disease Injury Incidence Prevalence Collaborators. Borges do Nascimento IJ, Regulatory, political, and legal concerns, 10. To enable potential health care cost reduction, 6. CNN was the most common model used for gastric problem classification or detection. One of the issues that hampers reproducibility of studies, and therefore scientific progress, is the lack oforiginal implementation (with proper documentation) of the methods and techniques, and the unavailability of the original data used to test the methods. The following data were extracted from the retrieved articles: publication information, journal name and impact factor, study characteristics, big data characteristics, outcomes, lessons and barriers for implementation, and main limitations. journal self-citations removed) received by a journal's published documents during the three previous years. in Machine learning and its potential applications to the genomic study of head and neck cancer-A systematic review. Furthermore, three reviews covered mental health, associated with the indicator suicide mortality rate [19,25,45]; three studies were related to the indicator probability of dying from any of cardiovascular, cancer, diabetes, or chronic renal disease [16,18,20,28,29]; and two studies were related to the indicator proportion of bloodstream infections due to antimicrobial-resistant organisms [26,33]. Quality assessment judgment using the AMSTAR 2 tool. We have been guided through the very complicated process swiftly and securely by simply following their concise instructions. Additionally, residual neural network and fully convolutional network were considered to be appropriate models for disease generation, classification, and segmentation. The purpose is to have a forum in which general doubts about the processes of publication in the journal, experiences and other issues derived from the publication of papers are resolved. Solutions such as those based on neural networks may be highly effective when presented with huge amounts of data, but their training and deployment costs as well as their opaqueness may not make them the best choice for a given health-related application. - Mr. Dhaval Joshi (Senior Product Manager at Tencent in Shenzhen, China). - Dr. Shalin Hai-Jew (Kansas State University, USA). As an academic publisher at the forefront of advancement for over 30 years, IGI Global OA provides quality,
expediate OA publishing with a top-of-the-line production system backed by the international Committee on
Publication Ethics (COPE). Lancet 2017 Sep 16;390(10100):1211-1259 [. April, https://preprints.jmir.org/preprint/27275, Israel Jnior AI tools associated with big data analytics in the care of patients with diabetes mellitus (DM) were assessed in six reviews that included 345 primary studies [15,20,32,38,40]. Through the application of artificial intelligence (AI) algorithms and machine learning (ML), big data analytics has potential to revolutionize health care, supporting clinicians, providers, and policymakers for planning or implementing interventions [1], faster disease detection, therapeutic decision support, outcome prediction, and increased personalized medicine, resulting in lower-cost, higher-quality care with better outcomes [1,2]. Doganer, A. A systematic literature review of machine learning in online personal health data. Lack of skills and training among professionals to collect, process, or extract data, 8. BMC Health Serv Res 2019 Nov 19;19(1):845 [, Ngiam KY, Khor IW. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.04.2021. [, Cassidy R, Singh NS, Schiratti P, Semwanga A, Binyaruka P, Sachingongu N, et al. Big Data Analytics for Healthcare is an international Open Access journal that publishes original research articles and review articles related to all areas of big data analytics in the healthcare research and practices. Two reviews assessed the use of ML algorithms for predicting suicidal behaviors. Reducing the bias error will improve the classification performance. Kasten, Joseph E. "Big Data Applications in Vaccinology,", Pabreja, Kavita and Akanksha Bhasin. Therefore, we urge the testing and assessment of supervised, unsupervised, and semisupervised methodologies, with explanation and interpretation to justify the results. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. The topics of interest includes bioinformatics, data analytic tools, sensor informatics, policy development and implementation, and also genomic-based analytics.. Big Data Analytics for Healthcare, -/2630-4767, started opeartions from Singapore in year 2018, publish paper in Biology Quarterly. Big data and machine learning algorithms for health-care delivery. The diversity of big data tools and ML algorithms requires proper standardization of protocols and comparative approaches. However, accuracy values and error rate, which is simply the complement of accuracy, are not adequate for skewed or imbalanced classification tasks (ie, when the distribution of observations in the training dataset across the classes is not equal), because of the bias toward the majority class. Of 11 studies, 8 reported sensitivity and specificity of 80.3% to 100% and 84%% to 99%, respectively; two reported accuracies of 78.7% and 81%; and one reported an area under the receiver operating curve (AUC) of 0.955 [15]. However, AI algorithm performance metrics used different standards, precluding objective comparison. Along with the 46 indicators listed in Textbox 1, we also included studies evaluating the use of big data during the COVID-19 pandemic. Inf Process Manage 2021 May;58(3):102481. Computer-driven analysis can easily handle missing data, examine variable mechanisms in complex systems, and employ essential tools for exploratory evaluations using voluminous input data. Further studies should focus on how big data analytics impact clinical outcomes and on creating proper methodological guidelines for reporting big data/ML studies, as well as using robust performance metrics to assess accuracy. Arch Clin Infect Dis 2020 May 10;15(2):e103232. The use of ML algorithms for early detection of psychiatric conditions was also reported [12,45]. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. Although research in this field has been growing exponentially in the last decade, the overall quality of evidence is found to be low to moderate.