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Wiltz JL, Lee B, Kaufmann R, Carney TJ, Davis K, Briss PA. Modernizing CDC's Practices and Culture for Better Data Sharing, Impact, and Transparency. Prev Chronic Dis 2024; 21:E18. [PMID: 38512778 PMCID: PMC10962273 DOI: 10.5888/pcd21.230200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Affiliation(s)
- Jennifer L Wiltz
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy, Mail Stop S107-8, Atlanta, GA 30341
- US Public Health Service, Bethesda, Maryland
| | - Brian Lee
- Office of the Chief Information Officer, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rachel Kaufmann
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Timothy J Carney
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Kailah Davis
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Peter A Briss
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
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Weberpals J, Wang SV. The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R. Pharmacoepidemiol Drug Saf 2024; 33:e5740. [PMID: 38173166 DOI: 10.1002/pds.5740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.
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Affiliation(s)
- Janick Weberpals
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Shirley V Wang
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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3
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Stead WW, Miller RA, Ohno-Machado L, Bakken S. JAMIA at 30: looking back and forward. J Am Med Inform Assoc 2023; 31:1-9. [PMID: 38134400 PMCID: PMC10746314 DOI: 10.1093/jamia/ocad215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 12/24/2023] Open
Affiliation(s)
- William W Stead
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Randolph A Miller
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Alexandria, VA 37232, United States
| | - Lucila Ohno-Machado
- Section of Biomedical Informatics & Data Science, Yale University School of Medicine, New Haven, CT 06520, United States
| | - Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, NY 10032, United States
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Cowie K, Rahmatullah A, Hardy N, Holub K, Kallmes K. Web-Based Software Tools for Systematic Literature Review in Medicine: Systematic Search and Feature Analysis. JMIR Med Inform 2022; 10:e33219. [PMID: 35499859 PMCID: PMC9112080 DOI: 10.2196/33219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 01/06/2022] [Accepted: 03/12/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices. OBJECTIVE This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools. METHODS We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. RESULTS Of the 53 SR tools found, 55% (29/53) were excluded, leaving 45% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46%. Giotto Compliance (27/30, 90%), DistillerSR (26/30, 87%), and Nested Knowledge (26/30, 87%) support the most features, followed by EPPI-Reviewer Web (25/30, 83%), LitStream (23/30, 77%), JBI SUMARI (21/30, 70%), and SRDB.PRO (VTS Software) (21/30, 70%). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42%) support direct search, only 7 (29%) offer dual extraction, and only 13 (54%) offer living/updatable reviews. CONCLUSIONS DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.
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Affiliation(s)
| | | | | | - Karl Holub
- Nested Knowledge, Saint Paul, MN, United States
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Read KB, Ganshorn H, Rutley S, Scott DR. Data-sharing practices in publications funded by the Canadian Institutes of Health Research: a descriptive analysis. CMAJ Open 2021; 9:E980-E987. [PMID: 34753787 PMCID: PMC8580829 DOI: 10.9778/cmajo.20200303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND As Canada increases requirements for research data management and sharing, there is value in identifying how research data are shared and what has been done to make them findable and reusable. This study aimed to understand Canada's data-sharing landscape by reviewing how data funded by the Canadian Institutes of Health Research (CIHR) are shared and comparing researchers' data-sharing practices to best practices for research data management and sharing. METHODS We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central published between 1946 and Dec. 31, 2019, that indicated that the research data underlying the results of the publication were shared. We analyzed each publication to identify how and where data were shared, who shared data and what documentation was included to support data reuse. RESULTS Of 4144 CIHR-funded publications identified, 1876 (45.2%) included accessible data, 935 (22.6%) stated that data were available via request or application, and 300 (7.2%) stated that data sharing was not applicable or possible; we found no evidence of data sharing in 1558 publications (37.6%). Frequent data-sharing methods included via a repository (1549 [37.4%]), within supplementary files (1048 [25.3%]) and via request or application (935 [22.6%]). Overall, 554 publications (13.4%) included documentation that would facilitate data reuse. INTERPRETATION Publications funded by the CIHR largely lack the metadata, access instructions and documentation to facilitate data discovery and reuse. Without measures to address these concerns and enhanced support for researchers seeking to implement best practices for research data management and sharing, much CIHR-funded research data will remain hidden, inaccessible and unusable.
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Affiliation(s)
- Kevin B Read
- Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta.
| | - Heather Ganshorn
- Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta
| | - Sarah Rutley
- Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta
| | - David R Scott
- Leslie & Irene Dubé Health Sciences Library (Read), University of Saskatchewan, Saskatoon, Sask.; Taylor Family Digital Library (Ganshorn), University of Calgary, Calgary, Alta.; University Library (Rutley), University of Saskatchewan, Saskatoon, Sask.; University Library (Scott), University of Lethbridge, Lethbridge, Alta
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6
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Balkányi L, Lukács L, Cornet R. Investigating the Scientific 'Infodemic' Phenomenon Related to the COVID-19 Pandemic. Yearb Med Inform 2021; 30:245-256. [PMID: 33882597 PMCID: PMC8416197 DOI: 10.1055/s-0041-1726483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVES The study aims at understanding the structural characteristics and content features of COVID-19 literature and public health data from the perspective of the 'Language and Meaning in Biomedicine' Working Group (LaMB WG) of IMIA. The LaMB WG has interest in conceptual characteristics, transparency, comparability, and reusability of medical information, both in science and practice. METHODS A set of methods were used (i) investigating the overall speed and dynamics of COVID-19 publications; (ii) characterizing the concepts of COVID-19 (text mining, visualizing a semantic map of related concepts); (iii) assessing (re)usability and combinability of data sets and paper collections (as textual data sets), and checking if information is Findable, Accessible, Interoperable, and Reusable (FAIR). A further method tested practical usability of FAIR requirements by setting up a common data space of epidemiological, virus genetics and governmental public health measures' stringency data of various origin, where complex data points were visualized as scatter plots. RESULTS Never before were that many papers and data sources dedicated to one pandemic. Worldwide research shows a plateau at ∼ 2,200 papers per week - the dynamics of areas of studies being slightly different. Ratio of epidemic modelling is rather low (∼1%). A few 'language and meaning' methods, such as using integrated terminologies, applying data and metadata standards for processing epidemiological and case-related clinical information and in general, principles of FAIR data handling could contribute to better results, such as improved interoperability and meaningful knowledge sharing in a virtuous cycle of continuous improvements.
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Affiliation(s)
- László Balkányi
- Medical Informatics Research and Development Center (MIRDC), Pannon University, Veszprém, Hungary
| | | | - Ronald Cornet
- Department of Medical Informatics, Amsterdam University Medical Center - University of Amsterdam, Amsterdam Public Health research institute, Amsterdam, The Netherlands
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Anthony N, Pellen C, Ohmann C, Moher D, Naudet F. Social media attention and citations of published outputs from re-use of clinical trial data: a matched comparison with articles published in the same journals. BMC Med Res Methodol 2021; 21:119. [PMID: 34092224 PMCID: PMC8182934 DOI: 10.1186/s12874-021-01311-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/30/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Data-sharing policies in randomized clinical trials (RCTs) should have an evaluation component. The main objective of this case-control study was to assess the impact of published re-uses of RCT data in terms of media attention (Altmetric) and citation rates. METHODS Re-uses of RCT data published up to December 2019 (cases) were searched for by two reviewers on 3 repositories (CSDR, YODA project, and Vivli) and matched to control papers published in the same journal. The Altmetric Attention Score (primary outcome), components of this score (e.g. mention of policy sources, media attention) and the total number of citations were compared between these two groups. RESULTS 89 re-uses were identified: 48 (53.9%) secondary analyses, 34 (38.2%) meta-analyses, 4 (4.5%) methodological analyses and 3 (3.4%) re-analyses. The median (interquartile range) Altmetric Attention Scores were 5.9 (1.3-22.2) for re-use and 2.8 (0.3-12.3) for controls (p = 0.14). No statistical difference was found on any of the components of in the Altmetric Attention Score. The median (interquartile range) numbers of citations were 3 (1-8) for reuses and 4 (1 - 11.5) for controls (p = 0.30). Only 6/89 re-uses (6.7%) were cited in a policy source. CONCLUSIONS Using all available re-uses of RCT data to date from major data repositories, we were not able to demonstrate that re-uses attracted more attention than a matched sample of studies published in the same journals. Small average differences are still possible, as the sample size was limited. However matching choices have some limitations so results should be interpreted very cautiously. Also, citations by policy sources for re-uses were rare. TRIAL REGISTRATION Registration: osf.io/fp62e.
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Affiliation(s)
- N. Anthony
- University Hospital of La Réunion, Saint-Denis, Reunion Island France
- Univ Rennes, CHU Rennes, Inserm, CIC 1414 [(Centre d’Investigation Clinique de Rennes)], F-35000 Rennes, France
| | - C. Pellen
- Univ Rennes, CHU Rennes, Inserm, CIC 1414 [(Centre d’Investigation Clinique de Rennes)], F-35000 Rennes, France
| | - C. Ohmann
- European Clinical Research Infrastructure Network, Düsseldorf, Germany
| | - D. Moher
- Ottawa Hospital Research Institute, Ottawa, Canada
| | - F. Naudet
- Univ Rennes, CHU Rennes, Inserm, CIC 1414 [(Centre d’Investigation Clinique de Rennes)], F-35000 Rennes, France
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Digan W, Névéol A, Neuraz A, Wack M, Baudoin D, Burgun A, Rance B. Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites. J Am Med Inform Assoc 2021; 28:504-515. [PMID: 33319904 PMCID: PMC7936396 DOI: 10.1093/jamia/ocaa261] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Indexed: 11/24/2022] Open
Abstract
Background The increasing complexity of data streams and computational processes in modern clinical health information systems makes reproducibility challenging. Clinical natural language processing (NLP) pipelines are routinely leveraged for the secondary use of data. Workflow management systems (WMS) have been widely used in bioinformatics to handle the reproducibility bottleneck. Objective To evaluate if WMS and other bioinformatics practices could impact the reproducibility of clinical NLP frameworks. Materials and Methods Based on the literature across multiple researcho fields (NLP, bioinformatics and clinical informatics) we selected articles which (1) review reproducibility practices and (2) highlight a set of rules or guidelines to ensure tool or pipeline reproducibility. We aggregate insight from the literature to define reproducibility recommendations. Finally, we assess the compliance of 7 NLP frameworks to the recommendations. Results We identified 40 reproducibility features from 8 selected articles. Frameworks based on WMS match more than 50% of features (26 features for LAPPS Grid, 22 features for OpenMinted) compared to 18 features for current clinical NLP framework (cTakes, CLAMP) and 17 features for GATE, ScispaCy, and Textflows. Discussion 34 recommendations are endorsed by at least 2 articles from our selection. Overall, 15 features were adopted by every NLP Framework. Nevertheless, frameworks based on WMS had a better compliance with the features. Conclusion NLP frameworks could benefit from lessons learned from the bioinformatics field (eg, public repositories of curated tools and workflows or use of containers for shareability) to enhance the reproducibility in a clinical setting.
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Affiliation(s)
- William Digan
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges Pompidou, Assistance publique-Hôpitaux de Paris, Paris, France
| | | | - Antoine Neuraz
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Necker Children's Hospital, Assistance publique-Hôpitaux de Paris, Paris, France
| | - Maxime Wack
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges Pompidou, Assistance publique-Hôpitaux de Paris, Paris, France
| | - David Baudoin
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges Pompidou, Assistance publique-Hôpitaux de Paris, Paris, France
| | - Anita Burgun
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges Pompidou, Assistance publique-Hôpitaux de Paris, Paris, France.,Department of Medical Informatics, Necker Children's Hospital, Assistance publique-Hôpitaux de Paris, Paris, France
| | - Bastien Rance
- INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université de Paris, Université Sorbonne Paris Cité, Paris, France.,Department of Medical Informatics, Hôpital Européen Georges Pompidou, Assistance publique-Hôpitaux de Paris, Paris, France
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Welty LJ, Rasmussen LV, Baldridge AS, Whitley EW. Facilitating reproducible research through direct connection of data analysis with manuscript preparation: StatTag for connecting statistical software to Microsoft Word. JAMIA Open 2020; 3:342-358. [PMID: 33215069 PMCID: PMC7660954 DOI: 10.1093/jamiaopen/ooaa043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/06/2020] [Accepted: 09/09/2020] [Indexed: 12/14/2022] Open
Abstract
Objectives To enhance reproducible research by creating a broadly accessible, free, open-source software tool for connecting Microsoft Word to statistical programs (R/R Markdown, Python, SAS, Stata) so that results may be automatically updated in a manuscript. Materials and Methods We developed StatTag for Windows as a Microsoft Word plug-in using C# and for macOS as a native application using Objective-C. Source code is available under the MIT license at https://github.com/stattag. Results StatTag links analysis file(s) (R/R Markdown, SAS, Stata, or Python) and a Word document, invokes the statistical program(s) to obtain results, and embeds selected output in the document. StatTag can accommodate multiple statistical programs with a single document and features an interface to view, edit, and rerun statistical code directly from Word. Discussion and Conclusion StatTag may facilitate reproducibility within increasingly multidisciplinary research teams, improve research transparency through review and publication, and complement data-sharing initiatives.
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Affiliation(s)
- Leah J Welty
- Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Luke V Rasmussen
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Abigail S Baldridge
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Eric W Whitley
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Helm E, Lin AM, Baumgartner D, Lin AC, Küng J. Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E1348. [PMID: 32093073 PMCID: PMC7068384 DOI: 10.3390/ijerph17041348] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/09/2020] [Accepted: 02/14/2020] [Indexed: 11/23/2022]
Abstract
Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.
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Affiliation(s)
- Emmanuel Helm
- Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria; (A.M.L.); (D.B.)
- Institute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, Austria;
| | - Anna M. Lin
- Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria; (A.M.L.); (D.B.)
| | - David Baumgartner
- Research Department Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria; (A.M.L.); (D.B.)
| | - Alvin C. Lin
- Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Josef Küng
- Institute for Applied Knowledge Processing, Johannes Kepler University, 4040 Linz, Austria;
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11
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Welton T, Indja BE, Maller JJ, Fanning JP, Vallely MP, Grieve SM. Replicable brain signatures of emotional bias and memory based on diffusion kurtosis imaging of white matter tracts. Hum Brain Mapp 2019; 41:1274-1285. [PMID: 31773802 PMCID: PMC7268065 DOI: 10.1002/hbm.24874] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/11/2019] [Accepted: 11/12/2019] [Indexed: 12/31/2022] Open
Abstract
Diffusion MRI (dMRI) is sensitive to anisotropic diffusion within bundles of nerve axons and can be used to make objective measurements of brain networks. Many brain disorders are now recognised as being caused by network dysfunction or are secondarily associated with changes in networks. There is therefore great potential in using dMRI measures that reflect network integrity as a future clinical tool to help manage these conditions. Here, we used dMRI to identify replicable, robust and objective markers that meaningfully reflect cognitive and emotional performance. Using diffusion kurtosis analysis and a battery of cognitive and emotional tests, we demonstrated strong relationships between white matter structure across networks of anatomically and functionally specific brain regions with both emotional bias and emotional memory performance in a large healthy cohort. When the connectivity of these regions was examined using diffusion tractography, the terminations of the identified tracts overlapped precisely with cortical loci relating to these domains, drawn from an independent spatial meta‐analysis of available functional neuroimaging literature. The association with emotional bias was then replicated using an independently acquired healthy cohort drawn from the Human Connectome Project. These results demonstrate that, even in healthy individuals, white matter dMRI structural features underpin important cognitive and emotional functions. Our robust cross‐correlation and replication supports the potential of structural brain biomarkers from diffusion kurtosis MRI to characterise early neurological changes and risk in individuals with a reduced threshold for cognitive dysfunction, with further testing required to demonstrate clinical utility.
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Affiliation(s)
- Thomas Welton
- Sydney Translational Imaging Laboratory, Heart Research Institute, The University of Sydney, Camperdown, New South Wales, Australia
| | - Ben E Indja
- Sydney Medical School, The University of Sydney, Camperdown, New South Wales, Australia
| | - Jerome J Maller
- Sydney Translational Imaging Laboratory, Heart Research Institute, The University of Sydney, Camperdown, New South Wales, Australia.,GE Healthcare, Richmond, Victoria, Australia
| | - Jonathon P Fanning
- Faculty of Medicine, The University of Queensland, Brisbane, New South Wales, Australia.,The Critical Care Research Group, The Prince Charles Hospital, Brisbane, New South Wales, Australia
| | - Michael P Vallely
- Sydney Medical School, The University of Sydney, Camperdown, New South Wales, Australia.,Department of Cardiothoracic Surgery, The Northern Beaches Hospital, Sydney, New South Wales, Australia
| | - Stuart M Grieve
- Sydney Translational Imaging Laboratory, Heart Research Institute, The University of Sydney, Camperdown, New South Wales, Australia.,Department of Radiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
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12
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Humphreys BL. New requirements for clinical trial transparency provide new opportunities for informatics research. J Am Med Inform Assoc 2019; 26:493-494. [PMID: 31087069 DOI: 10.1093/jamia/ocz047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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