healthcare analytics impact factor

2021/05/21

Azzopardi-Muscat, Marcos Andr Several studies reported significant improvement in disease diagnosis and event prediction using big data analytics tools, including remarkable enhancement of sepsis prediction using ML techniques [26]. Ideally, the parameters should be chosen for each specific task and dataset using a partition of the training set (ie, validation), which is different from the dataset used to train and to test the model. 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. 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]. Any conflict was resolved by a third party. However, the authors encouraged creating models using large datasets to increase prediction accuracy levels. One review focused on the diagnostic accuracy of AI systems in analyzing radiographic images for pulmonary tuberculosis, mostly referring to development instead of clinical evaluation [27]. Most of the studies were excluded after title and abstract analysis (n=1237), leaving 185 selected for full-text screening, and 35 [11-45] were ultimately included in the final analysis after applying the eligibility criteria according to the QUOROM guidelines [8] (Figure 1). 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. Expenses with data storage and transfer, 1. Studies with a review protocol tracking number were analyzed. April, https://preprints.jmir.org/preprint/27275, Israel Jnior This is an area that is ripe for development. (2021). [, Lee S, Mohr NM, Street WN, Nadkarni P. Machine learning in relation to emergency medicine clinical and operational scenarios: an overview. 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. We avoided reporting bias through the dual and blinded examination of systematic reviews and by having one review author standardizing the extracted data. Reducing the bias error will improve the classification performance. Although the review authors stated the potential of ML techniques in daily clinical practice, limitations were highlighted, including no external validation and reporting inconsistencies. International Collaboration accounts for the articles that have been produced by researchers from several countries. 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. The titles of these review protocols showed an intention to evaluate ML tools in diagnosis and prediction, the impact of telemedicine using ML techniques, and the use of AI-based disease surveillance [55]. World Health Organization, regional office for Europe. 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. Pabreja, Kavita, and Akanksha Bhasin. In Textbox 3, we summarize our recommendations for systematic reviews on the application of big data and ML for peoples health based on our experience, the findings of this systematic review, and inspired by Cunha et al [53]. Data standardization: concerns with limited interoperability, data obtention, mining, and sharing, along with language barriers, 4.

J King Saud Univ Comput Inf Sci 2020 Jul:In press [, Scardoni A, Balzarini F, Signorelli C, Cabitza F, Odone A. One review also assessed multiple sclerosis diagnosis. To improve data quality, structure, and accessibility by enabling the improvement of rapid acquisition of large volumes and types of data, in a transparent way, and the improvement of data error detection, 5. Arch Cardiol Mex 2018;88(3):178-189. 2020. 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. University of York Centre for Reviews and Dissemination. Results: The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Without knowing how and why the models achieve their results, applicability and trust of the models in real-world scenarios are severely compromised. 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. Publishing with IGI Global has been very productive for me. 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]. Lack of skills and training among professionals to collect, process, or extract data, 8. Important characteristics essential for replicability and external validation were not frequently available. URL: Rumsfeld JS, Joynt KE, Maddox TM. 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. A systematic review of predictive models for asthma development in children. The average number of weeks it takes to reach from manuscript acceptance to the first appearance of the article online (with DOI). 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. The ability to get the book delivered to the world is not less important than the writing of it. Netherlands Most were published in English in a first-quartile journal with an impact factor ranging from 0.977 to 17.679. 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.

Rule I of M (US) C on HR and the P of HITHP. The gap between demand and supply can be fulfilled by increasing voluntary blood donations. To improve patient-centric health care and to enhance personalized medicine, 3. 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. Another review provided moderate evidence that ML models can reach high performance standards in detecting health careassociated infections [33]. Submit Your Journal for Impact Factor Evaluation. Deep learning-based algorithms in screening of diabetic retinopathy: a systematic review of diagnostic performance. Additionally, all IGI Global published content is available in IGI Global's InfoSci, Copyright 1988-2022, IGI Global - All Rights Reserved, (10% discount on all e-books cannot be combined with most offers. IJBdN, MM, MG, NAM, and DNO designed the study. 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 study protocol is published on PROSPERO (CRD42020214048). However, the authors of these reviews concluded that achieving a methodologically precise predictive model is challenging and must consider multiple parameters. The response to the first edition was much greater than expected, I think due to the excellent marketing system of IGI Global. The use of a single default split of the input dataset with only one training/test split does not fit this requirement. Most of the reviews assessed performance values using big data tools and ML techniques, and demonstrated their applications in medical practice. 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. Researchers may also consider the practical aspect of a journal such as publication fees, acceptance rate, review speed. Although DSS are an important application of big data analytics and may benefit patient care [56-58], only two reviews assessed such systems [16,24]. Additionally, residual neural network and fully convolutional network were considered to be appropriate models for disease generation, classification, and segmentation. IGI Global recognizes that many researchers may not know where to begin when searching for OA funding opportunities. MM solved any disagreements. in Evolution of the number of published documents. The wrong or improper choice of parameters may make a highly effective method exhibit very poor behavior in a given task. Nat Rev Cardiol 2016 Jun;13(6):350-359. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. 10.2196/27275 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. Artif Intell Med 2019 Jul;98:109-134 [, Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI. "A Predictive Analytics Framework for Blood Donor Classification.". Pharmacotherapy 2018 Aug;38(8):813-821. journal self-citations removed) received by a journal's published documents during the three previous years. PLoS One 2019;14(9):e0221339 [, Patil S, Habib Awan K, Arakeri G, Jayampath Seneviratne C, Muddur N, Malik S, et al. Indeed, designing big data analysis and ML experiments involves elevated model complexity and commonly requires testing of several modeling algorithms [54]. [, Shatte ABR, Hutchinson DM, Teague SJ. Machine learning and data mining methods in diabetes research. [, Tomaselli Muensterman E, Tisdale JE. Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence. Discount is valid on purchases made directly through IGI Global Online Bookstore (, Kasten, Joseph E. "Big Data Applications in Vaccinology.". Measures the number of times articles from this journal have been saved to Mendeley to revisit later. Inaccuracy: issues with inconsistencies, lack of precision, and data timeliness, 5. 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. Marcolino, Hebatullah Mohamed Neurosci Biobehav Rev 2017 Sep;80:538-554. One review assessed the use of ML techniques for predicting cardiac arrest [42]. Healthcare data analytics and management. 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. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. One study under awaiting classification could not be retrieved. The diagnosis of ischemic stroke was associated with similar or better comparative accuracy for detecting large vessel occlusion compared with humans, depending on the AI algorithm employed [44]. Reference list screening did not retrieve any additional review. 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. Multimedia Appendix 2 shows the detailed results of the quality assessment of the 35 systematic reviews. Five reviews reported on AI, data mining, and ML in psychiatry/psychology [12,14,19,25,45], most commonly assessing these techniques in the diagnosis of mental disorders. BMJ 2017 Sep 21;358:j4008 [, Freeman JD, Blacker B, Hatt G, Tan S, Ratcliff J, Woolf TB, et al. The proper choice of an evaluation metric should be carefully determined, as these indices ought to be used by regulatory bodies for screening tests and not for diagnostic reasoning [52]. Abdulazeem HM, Inf Process Manage 2021 May;58(3):102481. 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]. The most common metric used to evaluate the performance of a classification predictive model is accuracy, which is calculated as the proportion of correct predictions in the test set divided by the total number of predictions that were made on the test set. Bias values and learning performances of different ensemble learning methods were compared. "Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods.". hangout liaison filgo noshir kellogg