Find out more: What is a good impact factor?
Many systematic reviews reported simple or inappropriate evaluation measures for the task at hand.
International Collaboration accounts for the articles that have been produced by researchers from several countries.
Israel Jnior Borges do Nascimento, Milena Soriano Marcolino, Hebatullah Mohamed Abdulazeem, Ishanka Weerasekara, Natasha Azzopardi-Muscat, Marcos Andr Gonalves, David Novillo-Ortiz.
Pabreja, Kavita, and Akanksha Bhasin. Over the years, I have seen how IGI Globals technological capabilities have come to the fore with their book publishing system, which vastly simplifies the process, particularly the uploading of manuscript contents, the double-blind peer review process, and the book proofing.
Therefore, we urge the testing and assessment of supervised, unsupervised, and semisupervised methodologies, with explanation and interpretation to justify the results. J Biomed Inform 2018 Dec;88:70-89 [, Wang W, Kiik M, Peek N, Curcin V, Marshall IJ, Rudd AG, et al.
Many reviews included data collected from electronic medical records, hospital information systems, or any databank that used individual patient data to create predictive models or evaluate collective patterns [12,13,16-21,24-27,30,33-35,37,38,40,42-45].
Abdulazeem HM, The authors noted several errors of AI use in diagnosing stroke [44]. Cambridge, MA: Academic Press; 2019:89-109. One study under awaiting classification could not be retrieved.
Inf Process Manage 2021 May;58(3):102481. Using computerized clinical decision support systems (DSS) significantly improves process outcomes in oncology [24]. One review assessed the use of ML techniques for predicting cardiac arrest [42]. URL: Rumsfeld JS, Joynt KE, Maddox TM.
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).
Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.04.2021. Furthermore, most studies provided a narrative description of results, requiring summarization. https://doi.org/10.1016/j.health.2021.100010, https://doi.org/10.1016/j.health.2021.100014, https://doi.org/10.1016/j.health.2022.100018, https://doi.org/10.1016/j.health.2022.100020, https://doi.org/10.1016/j.health.2022.100022, Danilo F. de Carvalho, Natal van Riel, https://doi.org/10.1016/j.health.2022.100024, Seyed Emadedin Hashemi, Parisa Yaghoubi, https://doi.org/10.1016/j.health.2022.100026, https://doi.org/10.1016/j.health.2022.100016, Meta-Health Stack: A new approach for breast cancer prediction, A deep learning approach for predicting early bounce-backs to the emergency departments, An explanatory analytics framework for early detection of chronic risk factors in pandemics, Mobile health evaluation: Taxonomy development and cluster analysis, A Markov model for inferring event types on diabetes patients data, A mathematical optimization model for location Emergency Medical Service (EMS) centers using contour lines, An artificial intelligence model for heart disease detection using machine learning algorithms. Discount is valid on purchases made directly through IGI Global Online Bookstore (, Kasten, Joseph E. "Big Data Applications in Vaccinology.".
Disaster Med Public Health Prep 2019 Apr;13(2):353-367.
Two reviews analyzed the use of big data analytics and AI in public health [22,30]. Quality assessment judgment using the AMSTAR 2 tool.
Regarding stroke, two systematic reviews evaluated using ML models for predicting outcomes and diagnosing cerebral ischemic events [43,44].
URL: Whitlock EP, Lin JS, Chou R, Shekelle P, Robinson KA.
elsevier earth access journal science open publish quarter issue its J Neurointerv Surg 2020 Feb;12(2):156-164. Supervised ML techniques were most frequently applied to predict cardiac arrest events, with clear evidence of regression techniques and SVM algorithms.
Tabary MY, Memariani A, Ebadati E. Chapter 3 - Developing a decision support system for big data analysis and cost allocation in national healthcare. For studies using structural neuroimaging to classify bipolar diseases and other diagnoses, the accuracy ranged from 52.13% to 100%, whereas studies using serum biomarkers reported an accuracy ranging from 72.5% to 77.5%.
Reference list screening did not retrieve any additional review. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases.
8080139, Copyright It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is. On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study.
High variability of results was observed across different ML techniques and approaches, even for the same disease or condition. BMJ 2011 Oct 18;343:d5928 [, Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. We also thank Raisa Eda de Resende, Edson Amaro Jnior, and Kaque Amncio Alvim for helping the group with data extraction and double-checking the input data. Doganer, Adem.
Even if the tuning of all methods is properly executed, this should be explicitly reported in the paper, with the exact values (or range of values) used for each parameter and the best choices used. Big data as a driver for clinical decision support systems: a learning health systems perspective.
Additionally, the process of tuning the hyperparameters of the algorithms is not uniformly reported. Name
[, Bridge J, Blakey JD, Bonnett L. A systematic review of methodology used in the development of prediction models for future asthma exacerbation. Gonalves MA,
The utilized tools included data originating from geographic information systems, social media interfaces, and disaster prediction modeling studies.
Limited awareness of big data analytics capabilities among health managers and health care professionals, 6. A systematic review of predictive models for asthma development in children.
This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal.
Authors should employ statistical significance tests to contrast the comparedstrategies in their experimental evaluation. World Health Organization. Nat Rev Cardiol 2016 Jun;13(6):350-359. Only two published systematic reviews evaluated the impact of big data analytics on the COVID-19 pandemic. Furthermore, implementing clinical DSS led to an average cost reduction of US $17,000 for lung cancer patients. The mission of the International Journal of Big Data and Analytics in Healthcare (IJBDAH) is to provide timely and innovative research on the ways in which big data is revolutionizing the medical and healthcare fields. PLoS One 2020;15(6):e0234722 [, Murray NM, Unberath M, Hager GD, Hui FK.
Using existing systematic reviews in complex systematic reviews.
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.
PLoS One 2019;14(9):e0221339 [, Patil S, Habib Awan K, Arakeri G, Jayampath Seneviratne C, Muddur N, Malik S, et al.
[, Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al.
This metric is easy to use and to interpret, as a single number summarizes the model capability.
(2021). Core and perfusion studies from RAPID-computed tomography and magnetic resonance imaging had the highest metrics for AI accuracy, above 80%, with some datasets showing 100% sensitivity to predict favorable perfusion mismatch. [, Cunha W, Mangaravite V, Gomes C, Canuto S, Resende E, Nascimento C, et al. The average number of weeks it takes to reach from manuscript acceptance to the first appearance of the article online (with DOI). The authors highlighted the potential for monitoring well-being, and providing an ecologically and cost-efficient evaluation of community mental health through social media and electronic records.
IGI Global recognizes that many researchers may not know where to begin when searching for OA funding opportunities. Publishing with IGI Global has been very productive for me.
Most of the reviews assessed performance values using big data tools and ML techniques, and demonstrated their applications in medical practice. Big data analytics can execute an operation on/process data within microseconds after generation of the dataset, allowing for real-time follow up [50,51]. - Dr. Linda Daniela (University of Latvia, Latvia), Request Information for Acquiring a Print Copy of, This journal was converted to full Gold Open Access on January 1, 2021. The objective of this criterion is to analyze whether the study assesses the capacity of generalization of each method compared in the experiments.
The study included broad descriptions of ML techniques and data types for detection, diagnosis, prognosis, treatment, support, and resulting public health implications.
The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world.
Circulation 2015 Nov 17;132(20):1920-1930 [.
Another dimension that may influence the decision for the practical use of a big data or a machine-learning method in a real practical situation is the ability to understand why the model has produced certain outputs (ie, explainability). Appraisal of the quality of evidence aligned with the Grading of Recommendations Assessment, Development and Evaluation method was reported in only one review [17].
In this paper an effort has been made to identify features in order of their importance that affect the decision of a person to become a blood donor.
Big data analytics tools handle complex datasets that traditional data processing systems cannot efficiently and economically store, manage, or process. The response to the first edition was much greater than expected, I think due to the excellent marketing system of IGI Global. Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Critical analysis of Big Data challenges and analytical methods. Machine learning in medicine.
Dagliati A, Tibollo V, Sacchi L, Malovini A, Limongelli I, Gabetta M, et al. Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.
Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. 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.
J Med Syst 2020 May 25;44(7):122 [, Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. 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.
[, Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. journal self-citations removed) received by a journal's published documents during the three previous years. Any impact factor or scientometric indicator alone will not give you the full picture of a science journal. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Use of big data and information and communications technology in disasters: an integrative review.
useof impact factors of science journals easier.
Most studies presented performance values, although no study assessed whether big data analytics or ML could improve the early detection of specific diseases. * Required.
[, Impact of Big Data Analytics on Peoples Health: Overview of Systematic Reviews and Recommendations for Future Studies, Secondary Use of Clinical Data for Research and Surveillance (219), Vol 23, No Biochemistry, Genetics and Molecular Biology, Pharmacology, Toxicology and Pharmaceutics, LaTeX Installation Guide Easy to Follow Steps to Install LaTeX, 6 Easy Steps to Create Your First LaTeX Document.
The experience with IGI Global has been positive because they are open to new ideas and offer a variety of opportunities for scientists to inform the society about current research, start discussions on emerging research trends, and provide a platform for the development of new knowledge. In studies assessing accuracy, the sensitivity ranged from 56% to 97%, specificity ranged from 36% to 95%, and the AUC ranged from 78% to 99%. The effectiveness of big data solutions and machine-learning methods is highly affected by the choice of the parameters of these methods (ie, parameter tuning).
Effectiveness of the solutions, as captured by accuracy-oriented measures, is not the only dimension that should be evaluated. Data security: problems with privacy, lack of transparency, integrity, and inherent data structure, 3.
"Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods.".
However, limitations exist. - Mr. Dhaval Joshi (Senior Product Manager at Tencent in Shenzhen, China). University of York Centre for Reviews and Dissemination.
Another pitfall identified among the included reviews was the lack of reporting the precise experimental protocols used for testing ML algorithms and the specific type of replication performed. By continuing you agree to the use of cookies. Washington, DC: National Academy of Sciences; 2009:334.
Powered by PlumX. Weerasekara, Natasha Many reviews did not evaluate bias. El Idrissi et al [18] used their own quality assessment tool and Luo et al [34] used an adapted version of the Critical Appraisal Skills Programme. The diversity of big data tools and ML algorithms require proper standardization of protocols and comparative approaches, and the process of tuning the hyperparameters of the algorithms is not uniformly reported.
The purposes of the reviews varied broadly. available at, Ristevski B, Chen M. Big data analytics in medicine and healthcare. The included reviews were published in 34 different journals from 2007 to 2020.
"A Predictive Analytics Framework for Blood Donor Classification,", Doganer, Adem. To publish open access, a publication fee (APC) needs to be met by the author or research funder. [, Pehrson L, Nielsen M, Ammitzbl Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review.
One focused on predictive analytics for identifying patients at risk of drug-induced QTc interval prolongation, discussing the efficacy of a DSS that has shown evidence of reduced prescriptions for QT intervalprolonging drugs. Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods.
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]. 9.
Unique records were uploaded onto the Covidence Platform (Veritas Health Innovation) for screening, data extraction, and quality assessment. Repetitions are essential to demonstrate the generalization of the investigated methods for multiple training and test sets, and to avoid any suspicion of a lucky (single) partition that favors the authors method.
In recent times, the development of the COVID-19 vaccine has captured the worlds attention as the primary tool to defeat the current pandemic.
It was quite a delight to be published with IGI Global. Another review assessing the use of big data in disaster preparedness evidenced that most existing methods are qualitative, covering the response phase of the disaster chain of events [11]. Impact factor (IF) is a scientometric factor based on the yearly average number of citations on articles published by a particular journal in the last two years.
Novillo-Ortiz D Impact of Big Data Analytics on Peoples Health: Overview of Systematic Reviews and Recommendations for Future Studies J Med Internet Res 2021;23(4):e27275 doi: Lastly, two studies reported accuracy levels ranging from 68% to 99.6% when using deep learning algorithms in the automatic detection of pulmonary nodules in computerized tomography images. Data structure: issues with fragmented data and incompatible or heterogeneous data formats, 2.
The gap between demand and supply can India faces numerous challenges to the meet ever-increasing demand of human blood so as to improve the health indicators across its rural and urban population.
10.2196/27275 Measures the number of times articles from this journal have been saved to Mendeley to revisit later. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances.
Another review focused on SARS-CoV-2 immunization, and proposed that AI could expedite vaccine discovery through studying the viruss capabilities, virulence, and genome using genetic databanks. 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. Front Digit Humanit 2018 May 1;5:8. paperpicks J Infect Public Health 2020 Aug;13(8):1061-1077 [, Luo G, Nkoy FL, Stone BL, Schmick D, Johnson MD.
As of November 30, 2020, many published protocols were retrieved through a standard search on PROSPERO.
Three studies reviewed AI in screening and diagnosing type 1 or type 2 DM, providing varied ranges of accuracy, sensitivity, and specificity [20,32,40].
Two researchers independently assessed the studies using the AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews 2) checklist, which includes the following critical domains, assessed in 16 items: protocol registered prior to review, adequacy of literature search, justification for excluded studies, risk of bias in included studies, appropriateness of meta-analytic methods, consideration of bias risk when interpreting results, and assessing the presence and likely impact of publication bias [10]. Arch Cardiol Mex 2018;88(3):178-189. One noted that techniques for diabetes self-management varied among the tools evaluated and reported mean values for its robust metrics [18].
Under OA, all the articles are published under a Creative Commons (CC BY) license; therefore, the authors or funding body will pay a one-time
article processing charge (APC) tooffset the costs of all of the activities associated with the publication of the article manuscript, including: *This service is only performed on article manuscripts with fully paid (not discounted or waived) APC fees. However, experimental/theoretical investigations, mathematical approaches, and computer-based studies hinge on handling sample size limitations and performing data imputation [48,49]. 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. It is also important to note that in scholarly publishing, utilizing an open access journal's resources and time with no intent to pay the open access Article Processing Charge (APC) if the article manuscript is accepted following the peer review process is considered to be unethical and exploitative. Big data and machine learning algorithms for health-care delivery. Two reviews assessed the use of ML algorithms for predicting suicidal behaviors.
Kasten, J. E. (2021). It is based on the idea that 'all citations are not created equal'.
Without knowing how and why the models achieve their results, applicability and trust of the models in real-world scenarios are severely compromised. However, AI algorithm performance metrics used different standards, precluding objective comparison.
jmir
Regulatory, political, and legal concerns, 10.
Pabreja, K., & Bhasin, A. The use of ML algorithms for early detection of psychiatric conditions was also reported [12,45]. Choose your measures wisely and justify your choice based on the aforementioned aspects of the task and the data.
