healthcare analytics impact factor

Artif Intell Med 2019 Jul;98:109-134 [, Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI. 2022 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. J Neurointerv Surg 2020 Feb;12(2):156-164. Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques.

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. [, Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, et al. Additionally, the included reviews in this systematic review addressed different health-related tasks; however, studies assessing the impact on clinical outcomes remain scarce. Systematic map and review of predictive techniques in diabetes self-management. [, Lee S, Mohr NM, Street WN, Nadkarni P. Machine learning in relation to emergency medicine clinical and operational scenarios: an overview.

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). Forty-six outcome target indicators emerged from the GPW13, covering a range of health issues [3]. 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.

Data were individually extracted by team members and cross-checked for accuracy by a second investigator. We avoided reporting bias through the dual and blinded examination of systematic reviews and by having one review author standardizing the extracted data. Most were published in English in a first-quartile journal with an impact factor ranging from 0.977 to 17.679. Submit Your Journal for Impact Factor Evaluation. Artificial intelligence applications in type 2 diabetes mellitus care: focus on machine learning methods. 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. Researchers may also consider the practical aspect of a journal such as publication fees, acceptance rate, review speed. Among the included studies, most models for predicting asthma development had less than 80% accuracy. Therefore, we urge the testing and assessment of supervised, unsupervised, and semisupervised methodologies, with explanation and interpretation to justify the results. "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?

Similarly, one study exploring the impact of DSS on quality care in oncology showed that implementing these systems might positively impact physician-prescribing behaviors, health care costs, and clinician workload. 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. Use of big data and information and communications technology in disasters: an integrative review.

However, the authors of these reviews concluded that achieving a methodologically precise predictive model is challenging and must consider multiple parameters. Abdulazeem HM,

Weerasekara, Natasha Big data analytics provide public health and health care with powerful instruments to gather and analyze large volumes of heterogeneous data. 2020. 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. A final summary score was given to each included record, rated as critically low, low, moderate, or high [10]. 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 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. Appropriateness to each appraisal feature was rated as yes, no, partial yes, not applicable, or unclear. The study protocol is published on PROSPERO (CRD42020214048). When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987).

Please note that IGI Global cannot schedule the article for publication or publish the article until payment has been received.

Along with the 46 indicators listed in Textbox 1, we also included studies evaluating the use of big data during the COVID-19 pandemic. To improve patient-centric health care and to enhance personalized medicine, 3. [, Cassidy R, Singh NS, Schiratti P, Semwanga A, Binyaruka P, Sachingongu N, et al. Sivarajah U, Kamal MM, Irani Z, Weerakkody V. Critical analysis of Big Data challenges and analytical methods. Variables included systolic blood pressure, body mass index, triglyceride levels, and others.

This journal aims to encourage the further development of applications and practice relating to the management and analysis of large amounts of data in the healthcare sector as IGI Global holds its journals to the highest ethical practices. 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. The wrong or improper choice of parameters may make a highly effective method exhibit very poor behavior in a given task. Inaccuracy: issues with inconsistencies, lack of precision, and data timeliness, 5.

The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. Measures the number of times articles from this journal have been saved to Mendeley to revisit later. Regarding stroke, two systematic reviews evaluated using ML models for predicting outcomes and diagnosing cerebral ischemic events [43,44]. Scores to identify patients at higher risk to develop QT-interval prolongation have been developed, and predictive analytics incorporated into clinical decision support tools have been tested for their ability to alert physicians of individuals who are at risk of or have QT-interval prolongation [16]. scientist or scholar. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.04.2021.

PLoS One 2019;14(9):e0221339 [, Patil S, Habib Awan K, Arakeri G, Jayampath Seneviratne C, Muddur N, Malik S, et al.

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. It is also impossible to assess whether the comparison is fair, as some methods may have been used at their maximum capacity and others not. 13.4.2021 Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal. This metric is easy to use and to interpret, as a single number summarizes the model capability. Measures the number of times articles from this journal have been downloaded or read since the journals launch date.

Find out more: What is a good impact factor? Most of the reviews were associated with the GPW13 indicator probability of dying from any cardiovascular disease, cancer, diabetes, chronic respiratory disease. This indicator outranks others because of the incidence, prevalence, premature mortality, and economic impact of these diseases [46]. Neurosci Biobehav Rev 2017 Sep;80:538-554. In 2018, the World Health Organization (WHO) proposed the expedited 13th General Programme of Work (GPW13), which was approved and adopted by its 194 Member States, focusing on measurable impacts on peoples health at the state level to transform public health with three core features: enhanced universal health coverage, health emergencies protection, and better health and well-being [3]. This procedure is known as cross-validation on the training set or nested cross-validation. Reference list screening did not retrieve any additional review. Big data and machine learning algorithms for health-care delivery. chadwick Hence, it becomes important to understand the attitude of population towards blood donations. BMC Med Res Methodol 2020 Feb 05;20(1):22 [, El Idrissi T, Idri A, Bakkoury Z. 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]. Healthcare data analytics and management. [, Tomaselli Muensterman E, Tisdale JE. This overview of systematic reviews updates the available evidence from multiple primary studies intersecting computer science, engineering, medicine, and public health. None of the 42 studies modeled the reincidence of exacerbation events, and overall accuracy performance was considered inadequate. The authors reported a mean AUC of 0.76 for risk score development and efficiency evaluation [42]. University of York Centre for Reviews and Dissemination. 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. Various studies assessed the ability of big data analytics to predict individual DM complications such as hypoglycemia, nephropathy, and others [15,32,38]. For topics on particular articles, maintain the dialogue through the usual channels with your editor. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. The average number of weeks it takes for an article to go from manuscript submission to the initial decision on the article, including standard and desk rejects. 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. The included studies covered all phases of the process. They covered over 2501 primary studies, involving at least 5,000,000 individuals. Background: Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Nonsystematic reviews, primary studies, opinions, short communications, nonscientific articles, conference abstracts, and reviews with big data inappropriately defined were excluded. 2019. Overlap made precise classification into WHO health indicators challenging, and four studies could not be categorized because they mainly described challenges or opportunities in big data analytics [23,39] or because they were related to the COVID-19 pandemic [35,37]. Evidence from these two systematic reviews, and those from the other reviews, are summarized in Textbox 2. DNO and NAM are staff members of the WHO. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Int J Inf Manage 2019 Jun;46:263-277. Nass SJ, Levit LA, Gostin LO.

Predictive analytics for identification of patients at risk for QT interval prolongation: a systematic review.

However, AI algorithm performance metrics used different standards, precluding objective comparison. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. International Collaboration accounts for the articles that have been produced by researchers from several countries. [, Gonalves WGE, Dos Santos M, Lobato FMF, Ribeiro-Dos-Santos , de Arajo GS. One assessed data mining and ML techniques in diagnosing COVID-19 cases. 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].

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]. We have been guided through the very complicated process swiftly and securely by simply following their concise instructions. Front Digit Humanit 2018 May 1;5:8. A systematic review of predictive models for asthma development in children. Diagnostics (Basel) 2019 Mar 07;9(1):29 [, Davidson L, Boland MR. We included complex reviews that assessed multiple interventions, different populations, and differing outcomes resulting from big data analytics on peoples health, and identified the challenges, opportunities, and best practices for future research. noshir kellogg paperpicks

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healthcare analytics impact factor