A Systematic Literature Review of Data Science, Data Analytics and Machine Learning Applied to Healthcare Engineering Systems
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Critical Reviews and Literature Reviews
UMKC Writing Studio 816.235.1146 [email protected] www.umkc.edu/writingstudio Critical Reviews and Literature Reviews What does a critical review include? ◘ The main ideas or arguments in the book or article. Don’t feel you have to mention everything: part of being an effective evaluator is A critical being able to extract the most important ideas from a source. review is not just a ◘ Your own evaluation of the book or article. Don’t just accept the summary. author’s statements without question. Ask yourself such questions as: How effective is the author’s argument? Is it clearly presented and explained? Is it biased in any way? Reading a book or article for a critical review: 1. First, read the assignment carefully. Your instructor may ask, for instance, that you read the book/article in the context of concepts you have covered in class, or that you focus on specific aspects of the book in your critical review. You can use this information to help guide your reading. 2. Before you begin reading, look at the title, as well as the chapter titles or article subheadings, to get an idea of what the author will focus on. You might start forming ideas about what you expect the book/article will say, and then, as you read, decide whether the book/article fulfills your expectations, or whether it leaves important questions unanswered. 3. Read actively. You might want to skim the book/article first, and then go back and take notes (remember to copy down page numbers!). You’ll have two main purposes in your reading. -
A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools
applied sciences Article A Comprehensive Framework to Reinforce Evidence Synthesis Features in Cloud-Based Systematic Review Tools Tatiana Person 1,* , Iván Ruiz-Rube 1 , José Miguel Mota 1 , Manuel Jesús Cobo 1 , Alexey Tselykh 2 and Juan Manuel Dodero 1 1 Department of Informatics Engineering, University of Cadiz, 11519 Puerto Real, Spain; [email protected] (I.R.-R.); [email protected] (J.M.M.); [email protected] (M.J.C.); [email protected] (J.M.D.) 2 Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review Citation: Person, T.; Ruiz-Rube, I.; Mota, J.M.; Cobo, M.J.; Tselykh, A.; tool focused on Computer Science, to implement evidence-based systematic review processes in Dodero, J.M. -
Guidelines for Including Grey Literature and Conducting Multivocal Literature Reviews in Software Engineering
This is a pre-print of the paper that has been accepted for publication in the Information and Software Technology (IST) journal: www.journals.elsevier.com/information-and-software-technology Guidelines for including grey literature and conducting multivocal literature reviews in software engineering Vahid Garousi Michael Felderer Mika V. Mäntylä Information Technology Group University of Innsbruck, Austria & M3S, Faculty of Information Technology Wageningen University, Netherlands Blekinge Institute of Technology, Sweden and Electrical Engineering [email protected] [email protected] University of Oulu, Oulu, Finland [email protected] Abstract: Context: A Multivocal Literature Review (MLR) is a form of a Systematic Literature Review (SLR) which includes the grey literature (e.g., blog posts, videos and white papers) in addition to the published (formal) literature (e.g., journal and conference papers). MLRs are useful for both researchers and practitioners since they provide summaries both the state-of-the art and –practice in a given area. MLRs are popular in other fields and have recently started to appear in software engineering (SE). As more MLR studies are conducted and reported, it is important to have a set of guidelines to ensure high quality of MLR processes and their results. Objective: There are several guidelines to conduct SLR studies in SE. However, several phases of MLRs differ from those of traditional SLRs, for instance with respect to the search process and source quality assessment. Therefore, SLR guidelines are only partially useful for conducting MLR studies. Our goal in this paper is to present guidelines on how to conduct MLR studies in SE. -
View of to Begin With, AHC Treats Each Data Object As a Separate the Data to the User, Frequently Achieved Using Clustering
BioData Mining BioMed Central Research Open Access Fast approximate hierarchical clustering using similarity heuristics Meelis Kull*1,2 and Jaak Vilo*1,2 Address: 1Institute of Computer Science, University of Tartu, Liivi 2, 50409 Tartu, Estonia and 2Quretec Ltd. Ülikooli 6a, 51003 Tartu, Estonia Email: Meelis Kull* - [email protected]; Jaak Vilo* - [email protected] * Corresponding authors Published: 22 September 2008 Received: 8 April 2008 Accepted: 22 September 2008 BioData Mining 2008, 1:9 doi:10.1186/1756-0381-1-9 This article is available from: http://www.biodatamining.org/content/1/1/9 © 2008 Kull and Vilo; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results: We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. -
Exploratory Analysis of Publons Metrics and Their Relationship with Bibliometric and Altmetric Impact
Exploratory analysis of Publons metrics and their relationship with bibliometric and altmetric impact José Luis Ortega Institute for Advanced Social Studies (IESA-CSIC), Córdoba, Spain, [email protected] Abstract Purpose: This study aims to analyse the metrics provided by Publons about the scoring of publications and their relationship with impact measurements (bibliometric and altmetric indicators). Design/methodology/approach: In January 2018, 45,819 research articles were extracted from Publons, including all their metrics (scores, number of pre and post reviews, reviewers, etc.). Using the DOI identifier, other metrics from altmetric providers were gathered to compare the scores of those publications in Publons with their bibliometric and altmetric impact in PlumX, Altmetric.com and Crossref Event Data (CED). Findings: The results show that (1) there are important biases in the coverage of Publons according to disciplines and publishers; (2) metrics from Publons present several problems as research evaluation indicators; and (3) correlations between bibliometric and altmetric counts and the Publons metrics are very weak (r<.2) and not significant. Originality/value: This is the first study about the Publons metrics at article level and their relationship with other quantitative measures such as bibliometric and altmetric indicators. Keywords: Publons, Altmetrics, Bibliometrics, Peer-review 1. Introduction Traditionally, peer-review has been the most appropriate way to validate scientific advances. Since the first beginning of the scientific revolution, scientific theories and discoveries were discussed and agreed by the research community, as a way to confirm and accept new knowledge. This validation process has arrived until our days as a suitable tool for accepting the most relevant manuscripts to academic journals, allocating research funds or selecting and promoting scientific staff. -
Diverse Convergent Evidence in the Genetic Analysis of Complex Disease
Dartmouth College Dartmouth Digital Commons Dartmouth Scholarship Faculty Work 6-30-2014 Diverse Convergent Evidence in the Genetic Analysis of Complex Disease: Coordinating Omic, Informatic, and Experimental Evidence to Better Identify and Validate Risk Factors Timothy H. Ciesielski Dartmouth College Sarah A. Pendergrass Pennsylvania State University, University Park Marquitta J. White Dartmouth College Nuri Kodaman Dartmouth College Rafal S. Sobota Dartmouth College Follow this and additional works at: https://digitalcommons.dartmouth.edu/facoa See next page for additional authors Part of the Medicine and Health Sciences Commons Dartmouth Digital Commons Citation Ciesielski, Timothy H.; Pendergrass, Sarah A.; White, Marquitta J.; Kodaman, Nuri; Sobota, Rafal S.; Huang, Minjun; Bartlett, Jacquelaine; Li, Jing; Pan, Qinxin; Gui, Jiang; Selleck, Scott B.; Amos, Christopher I.; Ritchie, Marylyn D.; Moore, Jason H.; and Williams, Scott M., "Diverse Convergent Evidence in the Genetic Analysis of Complex Disease: Coordinating Omic, Informatic, and Experimental Evidence to Better Identify and Validate Risk Factors" (2014). Dartmouth Scholarship. 2650. https://digitalcommons.dartmouth.edu/facoa/2650 This Article is brought to you for free and open access by the Faculty Work at Dartmouth Digital Commons. It has been accepted for inclusion in Dartmouth Scholarship by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [email protected]. Authors Timothy H. Ciesielski, Sarah A. Pendergrass, Marquitta J. White, Nuri Kodaman, Rafal S. Sobota, Minjun Huang, Jacquelaine Bartlett, Jing Li, Qinxin Pan, Jiang Gui, Scott B. Selleck, Christopher I. Amos, Marylyn D. Ritchie, Jason H. Moore, and Scott M. Williams This article is available at Dartmouth Digital Commons: https://digitalcommons.dartmouth.edu/facoa/2650 Ciesielski et al. -
Literature Reviews: a Guide
Literature Reviews: A Guide The purpose of a literature review is to, as the name suggests, “review” the literature surrounding a certain topic area. The word “literature” means “sources of information” or “research.” The literature will inform you about the research that has already been conducted on your chosen subject. If you are going to propose your own study, as when writing a thesis, dissertation, or research proposal, your literature review must be relevant to the work you plan to do. A literature review is NOT an annotated bibliography. You will not start a new section each time you talk about a new work. Several works can and probably will be mentioned in the same paragraph. While an annotated bibliography only demonstrates how particular sources will be used in a study, a literature review also shows how the sources interact with each other. At the end of your literature review, you will provide a works cited, bibliography, or references page. The format will depend on your field. Tasks you may want to accomplish in your literature review: --Show where you think previous research has left gaps --Distinguish various theoretical strains, and specify which theory your work will be in line with, if you are proposing a study. --Define terms that will be important for your study, if you will be proposing. While specific requirements may differ depending on your discipline, professor, and assignment, there are some general guidelines that usually apply. As with any assignment, always check with your professor for you own specific requirements. A few general rules: • Do not procrastinate! Start working as early as possible. -
Genomic Analyses with Biofilter 2.0: Knowledge Driven
Pendergrass et al. BioData Mining 2013, 6:25 http://www.biodatamining.org/content/6/1/25 BioData Mining SOFTWARE ARTICLE Open Access Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development Sarah A Pendergrass, Alex Frase, John Wallace, Daniel Wolfe, Neerja Katiyar, Carrie Moore and Marylyn D Ritchie* * Correspondence: [email protected] Abstract Center for Systems Genomics, Department of Biochemistry and Background: The ever-growing wealth of biological information available through Molecular Biology, The Pennsylvania multiple comprehensive database repositories can be leveraged for advanced State University, Eberly College of analysis of data. We have now extensively revised and updated the multi-purpose Science, The Huck Institutes of the Life Sciences, University Park, software tool Biofilter that allows researchers to annotate and/or filter data as well as Pennsylvania, PA, USA generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information. Results: Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories. -
No-Boundary Thinking in Bioinformatics
Huang et al. BioData Mining 2013, 6:19 http://www.biodatamining.org/content/6/1/19 BioData Mining REVIEW Open Access No-boundary thinking in bioinformatics research Xiuzhen Huang1*, Barry Bruce2, Alison Buchan3, Clare Bates Congdon4, Carole L Cramer5, Steven F Jennings6, Hongmei Jiang7, Zenglu Li8, Gail McClure9, Rick McMullen10, Jason H Moore11, Bindu Nanduri12, Joan Peckham13, Andy Perkins14, Shawn W Polson15, Bhanu Rekepalli16, Saeed Salem17, Jennifer Specker18, Donald Wunsch19, Donghai Xiong20, Shuzhong Zhang21 and Zhongming Zhao22 * Correspondence: [email protected] Abstract 1Department of Computer Science, Arkansas State University, Jonesboro, Currently there are definitions from many agencies and research societies defining AR 72467, USA “bioinformatics” as deriving knowledge from computational analysis of large volumes of Full list of author information is biological and biomedical data. Should this be the bioinformatics research focus? We will available at the end of the article discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT). Keywords: No-boundary thinking, Human infrastructure The Big-data paradigm Today’s data-intensive computing (“big data”) was advocated by the 1998 Turing Award1 recipient, Jim Gray, as the fourth paradigm for scientific discovery [1] after direct experimentation, theoretical mathematical physics and chemistry, and computer simulation. With the rapid advance of biotechnologies and development of clinical record systems, we have witnessed an exponential growth of data ranging from “omics” (such as genomics, proteomics, metabolomics, and pharmacogenomics), imaging data, to electronic medical record data. Last year, the federal funding agencies National Institutes of Health (NIH) and National Science Foundation (NSF) exer- cised a joint effort to launch big-data initiatives and consortia to promote and support big-data projects [2]. -
Biodata Mining Biomed Central
BioData Mining BioMed Central Research Open Access Search extension transforms Wiki into a relational system: A case for flavonoid metabolite database Masanori Arita*1,2,3 and Kazuhiro Suwa1 Address: 1Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha 5-1-5 CB05, Kashiwa, Japan, 2Metabolome Informatics Unit, Plant Science Center, RIKEN, Japan and 3Institute for Advanced Biosciences, Keio University, Japan Email: Masanori Arita* - [email protected]; Kazuhiro Suwa - [email protected] * Corresponding author Published: 17 September 2008 Received: 23 May 2008 Accepted: 17 September 2008 BioData Mining 2008, 1:7 doi:10.1186/1756-0381-1-7 This article is available from: http://www.biodatamining.org/content/1/1/7 © 2008 Arita and Suwa; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: In computer science, database systems are based on the relational model founded by Edgar Codd in 1970. On the other hand, in the area of biology the word 'database' often refers to loosely formatted, very large text files. Although such bio-databases may describe conflicts or ambiguities (e.g. a protein pair do and do not interact, or unknown parameters) in a positive sense, the flexibility of the data format sacrifices a systematic query mechanism equivalent to the widely used SQL. Results: To overcome this disadvantage, we propose embeddable string-search commands on a Wiki-based system and designed a half-formatted database. -
Coreviewing and Ghostwriting by Early Career Researchers in the Peer Review of Manuscripts Gary S. Mcdowell
bioRxiv preprint doi: https://doi.org/10.1101/617373; this version posted April 26, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Coreviewing and ghostwriting by early career researchers in the peer review of manuscripts Gary S. McDowell 1,* , John Knutsen 2 , June Graham 3 , Sarah K. Oelker 3 and Rebeccah S. Lijek 3,* Author affiliations: 1 The Future of Research, Inc., Abington, MA 02351 2 Harvard University, Cambridge, MA 02138 3 Mount Holyoke College, South Hadley, MA 01075 Correspondence: *Email: [email protected] ; [email protected] ; [email protected] bioRxiv preprint doi: https://doi.org/10.1101/617373; this version posted April 26, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract The goal of this study is to shed light on the involvement of early career researchers (ECRs) during peer review of manuscripts for publication in journals. In particular, we sought to better understand how commonly ECRs contribute ideas and/or text to peer review reports when they are not the invited reviewer (“coreview”), and how commonly ECRs do not receive named credit to the journal editorial staff for these scholarly efforts (“ghostwrite”). First, we evaluated 1,952 publications in the peerreviewed literature generated by exhaustive search terms that combined synonyms of “early career researcher” and “peer re view” and found no previous studies about ECRs ghostwriting peer review reports. -
Sample Literature Review
Running head: LITERATURE REVIEW 1 A literature review is a survey of scholarly sources that provides an overview of a particular topic. It generally follows a discussion of the paper’s thesis statement or the study’s goals or purpose. Format your title page according to your university guidelines. *This sample paper was adapted by the Writing Center from Key, K.L., Rich, C., DeCristofaro, C., Collins, S. (2010). Use of Propofol and emergence agitation in children: A literature review. AANA Journal, 78(6). Retrieved from www.aana.com. Used by permission. LITERATURE REVIEW 2 Use of Propofol and Emergence Agitation in Children: A Literature Review In 1961, Eckenhoff, Kneale, and Dripps described the phenomenon of emergence agitation (EA), reporting signs of hyperexcitation in patients emerging from ether, cyclopropane, or ketamine anesthesia. EA during recovery from general anesthesia has been identified as a frequent problem in the pediatric population. In children, EA has been described as a mental disturbance that consists of confusion, hallucinations, and delusions manifested by moaning, restlessness, involuntary physical activity, and thrashing about in bed (Sikich & Lerman, 2004). The overall rate for EA in children is in the range of 10% to 67% (Aouad & Nasr, 2005), which includes a period of severe restlessness, disorientation, and/or inconsolable crying during anesthesia emergence (Cole, Murray, McAllister, & Hirschberg, 2002). EA was reported as a problem in general anesthesia recovery before the development of the modern inhalational agents (sevoflurane and desflurane), and EA has been shown to occur with the use of all anesthetic gases. The increased use of sevoflurane and desflurane in recent years has been associated with a higher incidence of EA compared with isoflurane and halothane.