Among the 12 reviews that performed any quality assessment, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used in four reviews demonstrating an overall low risk of bias [14,16,27,28], whereas other tools assessed the risk of bias in studies not specifically aiming at diagnostic accuracy features. J Bus Res 2017 Jan;70:263-286. The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). Quality of Reporting of Meta-analyses. MM solved any disagreements. We only considered studies in which the search was performed in at least two databases, and included a description of the search strategy and the methodology used for study selection and data extraction. This journal offers authors the option to publish their research via open access. Another review provided moderate evidence that ML models can reach high performance standards in detecting health careassociated infections [33]. 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. A systematic review of machine learning models for predicting outcomes of stroke with structured data. 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). Acta Inform Med 2018 Dec;26(4):258-264 [, Layeghian Javan S, Sepehri MM, Aghajani H. Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework. We also schematically represent the evidence and gaps from these reviews as an overall synthesis. We avoided reporting bias through the dual and blinded examination of systematic reviews and by having one review author standardizing the extracted data. BMJ 2011 Oct 18;343:d5928 [, Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. 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: Gastric tissue disease and the usability of deep learning techniques were evaluated in one study [36]. To provide better forms to manage population health either through early detection of diseases or establishing ways to support health policy makers. However, the authors encouraged creating models using large datasets to increase prediction accuracy levels. Analyses of convolutional neural network (CNN) algorithms were limited, but systems using CNNs reported performance metrics on average 8% to 10% greater than those of ML employing RF, with up to 85% mean sensitivity for automatic large vessel occlusion detection. 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. To enhance health-threat detection plans by governmental entities, 10. By continuing you agree to the use of cookies. The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. This overview of systematic reviews updates the available evidence from multiple primary studies intersecting computer science, engineering, medicine, and public health. Hence, it becomes important to understand the attitude of population towards blood donations. 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]. Measures the number of times articles from this journal have been downloaded or read since the journals launch date. [, 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 Reducing the bias error will improve the classification performance. However, limitations exist. In: Dey N, Ashour AS, Bhat C, Fong SJ, editors. One review assessed the use of ML techniques for predicting cardiac arrest [42]. The artifacts made availablemustbe sufficient to ensure that published results can be accurately reproduced. The objective of this criterion is to analyze whether the study assesses the capacity of generalization of each method compared in the experiments. 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. The average number of weeks it takes for an article to go through the editorial review process for this journal, including standard and desk rejects. J Am Med Inform Assoc 2019 Jun 01;26(6):561-576 [, Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and opportunities of big data in health care: a systematic review. The other evaluated the use of data-driven tools for predicting blood glucose dynamics and the impact of ML and data mining [20], describing the input parameters used among data-driven analysis models. Only three reviews included meta-analyses, and one included a randomized clinical trial; the others were based on cohort studies. Data structure: issues with fragmented data and incompatible or heterogeneous data formats, 2. 2020. Novillo-Ortiz, https://www.who.int/about/what-we-do/GPW13_WHO_Impact_Framework_Indicator_Metadata.pdf, https://www.euro.who.int/en/health-topics/health-policy/european-programme-of-work/about-the-european-programme-of-work/european-programme-of-work-20202025-united-action-for-better-health-in-europe, Add this article to your Mendeley library, Number of persons affected by disasters (per 100,000 population), Domestic general government health expenditure (% of general government expenditure), Prevalence of stunting in children under 5 (%), Prevalence of wasting in children under 5 (%), Prevalence of overweight in children under 5 (%), Maternal mortality ratio (per 100,000 live births), Proportion of births attended by skilled health personnel (%), Under 5 mortality rate (per 1000 live births), Neonatal mortality rate (per 1000 live births), New HIV infections (per 1000 uninfected population), Tuberculosis incidence (per 100,000 population), Malaria incidence (per 1000 population at risk), Hepatitis B incidence (measured by surface antigen [HBsAg] prevalence among children under 5 years), Number of people requiring interventions against neglected tropical diseases (NTDs), Probability of dying from any of cardiovascular disease (CVD), cancer, diabetes, chronic renal disease (CRD) (aged 30-70 years) (%), Suicide mortality rate (per 100,000 population), Coverage of treatment interventions for substance-use disorders (%), Total alcohol per capita consumption in adults aged >15 years (liters of pure alcohol), Road traffic mortality rate (per 100,000 population), Proportion of women (aged 15-49 years) having need for family planning satisfied with modern methods (%), Universal Health Coverage (UHC) Service Coverage Index, Population with household expenditures on health >10% of total household expenditure or income (%), Mortality rate attributed to air pollution (per 100,000 population), Mortality rate attributed to exposure to unsafe water, sanitation, and hygiene (WASH) services (per 100,000 population), Mortality rate from unintentional poisoning (per 100,000 population), Prevalence of tobacco use in adults aged 15 years (%), Proportion of population covered by all vaccines included in national programs (diphtheria-tetanus-pertussis vaccine, measles-containing-vaccine second dose, pneumococcal conjugated vaccine) (%), Proportion of health facilities with essential medicines available and affordable on a sustainable basis (%), Density of health workers (doctors, nurse and midwives, pharmacists, dentists per 10,000 population), International Health Regulations capacity and health emergency preparedness, Proportion of bloodstream infections due to antimicrobial-resistant organisms (%), Proportion of children under 5 years developmentally on track (health, learning, and psychosocial well-being) (%), Proportion of women (aged 15-49 years) subjected to violence by current or former intimate partner (%), Proportion of women (aged 15-49 years) who make their own decisions regarding sexual relations, contraceptive use, and reproductive health care (%), Proportion of population using safely managed drinking-water services (%), Proportion of population using safely managed sanitation services and hand-washing facilities (%), Proportion of population with primary reliance on clean fuels (%), Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (g/m, Proportion of children (aged 1-17 years) experiencing physical or psychological aggression (%), Vaccine coverage for epidemic-prone diseases, Proportion of vulnerable people in fragile settings provided with essential health services (%), Prevalence of raised blood pressure in adults aged 18 years, Effective policy/regulation for industrially produced trans-fatty acids, Number of cases of poliomyelitis caused by wild poliovirus, Patterns of antibiotic consumption at the national level. 4 (2021): Intensive Care Med 2020 Mar;46(3):383-400 [, Harris M, Qi A, Jeagal L, Torabi N, Menzies D, Korobitsyn A, et al. Data Source: Scopus, Explore, visually communicate and make sense of data with our, Metrics based on Scopus data as of April 2022. Many systematic reviews reported simple or inappropriate evaluation measures for the task at hand. 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. "Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods,", Transformative Open Access (Read & Publish), Table of Contents - Latest Published Articles, How are Predatory Publishers Preying on Uninformed Scholars? This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Appraisal of the quality of evidence aligned with the Grading of Recommendations Assessment, Development and Evaluation method was reported in only one review [17]. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. Abdulazeem, Ishanka The choice of the tests should also reflect the characteristics of the data (ie, determining whether the data follow a normal distribution). 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. A journal impact factor is frequently used as a proxy for the relative importance of a journal within its field. To assist researchers in covering the costs of the APC in OA publishing, there are various sources of OA funding. The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. Variables included systolic blood pressure, body mass index, triglyceride levels, and others. 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. Nat Rev Cardiol 2016 Jun;13(6):350-359. It was quite a delight to be published with IGI Global. 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. Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Critical analysis of Big Data challenges and analytical methods. scientist or scholar. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Primary studies on COVID-19 are lacking, which indicates an opportunity to apply big data and ML to this and future epidemics/pandemics [35,37]. Forty-six outcome target indicators emerged from the GPW13, covering a range of health issues [3]. HA, IW, and IJBdN performed first- and second-stage screening, and extracted the presented data. 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. Authors shoulduse experimental protocols based on cross-validation or multiple training/validation/test splits of the employed datasets with more than one repetition of the experimental procedure. This research uses extensive visualization techniques to get an insight into potential blood donor characteristics and then applies classification technique to classify youth of an Indian state university as donor or non-donor. The study protocol is published on PROSPERO (CRD42020214048). 7. Among detection methodologies, rule-based and natural language processing methods were deemed to have superior diagnostic performance based of elevated accuracy and positive predictive value [41]. Four studies lacked precision about the origin of the datasets used in their analysis or did not specifically use patient data in the investigation [23,37,39,41]. Methods: Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. High variability of results was observed across different ML techniques and approaches, even for the same disease or condition. This metric is easy to use and to interpret, as a single number summarizes the model capability. PLoS One 2019;14(9):e0221339 [, Patil S, Habib Awan K, Arakeri G, Jayampath Seneviratne C, Muddur N, Malik S, et al. Big data analytics to improve cardiovascular care: promise and challenges. 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. Data were individually extracted by team members and cross-checked for accuracy by a second investigator. Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year. Inf Process Manage 2021 May;58(3):102481. 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). Therefore, we urge the testing and assessment of supervised, unsupervised, and semisupervised methodologies, with explanation and interpretation to justify the results. 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. 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. Two reviews covered DM control and the clinical management of DM patients [32,40]. 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. Statistical tests are essential to assess whether the performance of the analyzed methods in the sample (ie, the considered datasets) is likely to reflect, with certain confidence, their actual performance in the whole population. Nass SJ, Levit LA, Gostin LO. Two reviews assessed the use of ML algorithms for predicting suicidal behaviors. These studies also described useful clinical features for creating prediction and diagnostic models, such as patient clinical data, electrocardiogram characteristics, and cardiac biomarkers. Generally, they (1) outlined AI applications in different medical specialties; (2) analyzed features for the detection, prediction, or diagnosis of multiple diseases or conditions; or (3) pinpointed challenges and opportunities. 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]. Conclusions: Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Israel Jnior Borges do Nascimento, Milena Soriano Marcolino, Hebatullah Mohamed Abdulazeem, Ishanka Weerasekara, Natasha Azzopardi-Muscat, Marcos Andr Gonalves, David Novillo-Ortiz. Only two published systematic reviews evaluated the impact of big data analytics on the COVID-19 pandemic. received by a journal and the importance or prestige of the journals where Neurosci Biobehav Rev 2017 Sep;80:538-554. 9. journal self-citations removed) received by a journal's published documents during the three previous years. Reference list screening did not retrieve any additional review. [, Shatte ABR, Hutchinson DM, Teague SJ. I know they have a huge readership across the world. Two reviews reported the application of big data analytics and ML to better understand the current novel coronavirus pandemic [35,37]. Confidence in the results was rated as critically low for 25 reviews, as low for 7 reviews, and as moderate for 3 reviews. 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]. (2021). As such, they are key to support any claim of superiority of a particular method over others. However, AI algorithm performance metrics used different standards, precluding objective comparison. Disaster Med Public Health Prep 2019 Apr;13(2):353-367. 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. That study merged discussions of deep learningbased drug screening for predicting the interaction between protein and ligands, and using imaging results linked to AI tools for detecting SARS-CoV-2 infections. To improve patient-centric health care and to enhance personalized medicine, 3. 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%. BMC Med Res Methodol 2020 Feb 05;20(1):22 [, El Idrissi T, Idri A, Bakkoury Z. Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. In this study, different models were created to reduce bias by ensemble learning methods. This study indicates that these methods have potential impacts for early recognition of the disease, increasing quality of life, and allowing prompt pharmacological and nonpharmacological intervention. [, Deo RC. Diagnostics (Basel) 2019 Nov 29;9(4):207 [, Chaki J, Thillai Ganesh S, Cidham S, Ananda Theertan S. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. World Health Organization. The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. Rule I of M (US) C on HR and the P of HITHP. The low to moderate quality of evidence suggests that big data analytics has moderate to high accuracy for the (1) diagnosis and prediction of complications of DM, (2) diagnosis of mental diseases, (3) prediction of suicidal behaviors, and (4) diagnosis of chronic diseases. 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%. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values. Kasten, J. E. (2021).
The included studies covered all phases of the process. "Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods.". When the tuning information is missing or absent, it is impossible to determine whether the methods have been implemented appropriately and if they have achieved their maximum potential in a given task. JMIR Med Inform 2016 Nov 21;4(4):e38 [, Klarenbeek SE, Weekenstroo HH, Sedelaar JM, Ftterer JJ, Prokop M, Tummers M. The effect of higher level computerized clinical decision support systems on oncology care: a systematic review. Edited by R Kukafka, G Eysenbach; submitted 19.01.21; peer-reviewed by Y Mejova, A Benis; comments to author 09.02.21; revised version received 19.02.21; accepted 24.03.21; published 13.04.21. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. Thus, evidence of applicability in daily medical practice is still needed. For topics on particular articles, maintain the dialogue through the usual channels with your editor. 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. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. University of York Centre for Reviews and Dissemination. Powered by PlumX. With a streamlined publishing process, flexible funding options, and more,
researchers can freely and immediately share their peer-reviewed research with the world. Most of the reviews assessed performance values using big data tools and ML techniques, and demonstrated their applications in medical practice. It is based on the idea that 'all citations are not created equal'. Inaccuracy: issues with inconsistencies, lack of precision, and data timeliness, 5. PMCID: Cambridge, MA: Academic Press; 2019:89-109. Additionally, residual neural network and fully convolutional network were considered to be appropriate models for disease generation, classification, and segmentation. Precision (also called the positive predictive value), which captures the fraction of correctly classified instances among the instances predicted for a given class (eg, sick); recall or sensitivity, which captures the fraction of instances of a class (eg, sick) that were correctly classified; and F-measure, the harmonic mean of precision and recall calculated per class of interest, are more robust metrics for several practical situations. Bias values and learning performances of different ensemble learning methods were compared. Multimedia Appendix 2 shows the detailed results of the quality assessment of the 35 systematic reviews. One assessed data mining and ML techniques in diagnosing COVID-19 cases. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods. 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]. As authors, we are very grateful to IGI Global for all of their efforts on our behalf. ScienceDirect is a registered trademark of Elsevier B.V. ScienceDirect is a registered trademark of Elsevier B.V. Int J Inf Manage 2019 Jun;46:263-277. * Required. Big data analytics tools handle complex datasets that traditional data processing systems cannot efficiently and economically store, manage, or process.
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