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Akintola AA, Aborode AT, Hamza MT, Amakiri A, Moore B, Abdulai S, Iyiola OA, Sulaimon LA, Effiong E, Ogunyemi A, Dosunmu B, Maigoro AY, Lawal O, Raheem K, Hwang UW. Bioinformatics proficiency among African students. FRONTIERS IN BIOINFORMATICS 2024; 4:1328714. [PMID: 38966162 PMCID: PMC11222312 DOI: 10.3389/fbinf.2024.1328714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/25/2024] [Indexed: 07/06/2024] Open
Abstract
Bioinformatics, the interdisciplinary field that combines biology, computer science, and data analysis, plays a pivotal role in advancing our understanding of life sciences. In the African context, where the diversity of biological resources and healthcare challenges is substantial, fostering bioinformatics literacy and proficiency among students is important. This perspective provides an overview of the state of bioinformatics literacy among African students, highlighting the significance, challenges, and potential solutions in addressing this critical educational gap. It proposes various strategies to enhance bioinformatics literacy among African students. These include expanding educational resources, fostering collaboration between institutions, and engaging students in research projects. By addressing the current challenges and implementing comprehensive strategies, African students can harness the power of bioinformatics to contribute to innovative solutions in healthcare, agriculture, and biodiversity conservation, ultimately advancing the continent's scientific capabilities and improving the quality of life for her people. In conclusion, promoting bioinformatics literacy among African students is imperative for the continent's scientific development and advancing frontiers of biological research.
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Affiliation(s)
- Ashraf Akintayo Akintola
- School of Industrial Technology Advances, Kyungpook National University, Daegu, Republic of Korea
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
| | - Abdullahi Tunde Aborode
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Department of Chemistry, Mississippi State University, Starkville, MS, United States
| | - Muhammed Taofiq Hamza
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Green Climate Fund, Incheon, Republic of Korea
| | - Augustine Amakiri
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- ProCogia, Vancouver, BC, Canada
| | - Benjamin Moore
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Suliat Abdulai
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Department of Biochemistry, Fountain University, Osogbo, Nigeria
| | | | - Lateef Adegboyega Sulaimon
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Department of Biochemistry, Crescent University, Abeokuta, Nigeria
| | - Effiong Effiong
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Department of Medical Laboratory Sciences, Babcock University, Ilishan-Remo, Nigeria
| | - Adedeji Ogunyemi
- Center for Biotechnology and Genomics, Texas Tech University, Lubbock, TX, United States
| | | | - Abdulkadir Yusif Maigoro
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Department of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Opeyemi Lawal
- Department of Food Science, University of Guelph, Guelph, ON, Canada
| | - Kayode Raheem
- NOBLEKINMAT Ltd. Bioinformatics Research Group, Ibadan, Nigeria
- Cancer Research Artificial Intelligence (CARESAI), Hobart, Australia
| | - Ui Wook Hwang
- School of Industrial Technology Advances, Kyungpook National University, Daegu, Republic of Korea
- Department of Biology, Teachers College and Institute for Phylogenomics and Evolution, Kyungpook National University, Daegu, Republic of Korea
- Institute for Korean Herb-Bio Convergence Promotion, Kyungpook National University, Daegu, Republic of Korea
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Jo HS, Kim WJ, Park Y, Hwang YS, Han SS, Heo YJ, Moon D, Kim SK, Lee CY. Study Protocol for a Hospital-to-Home Transitional Care for Older Adults Hospitalized with Chronic Obstructive Pulmonary Disease in South Korea: A Randomized Controlled Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6507. [PMID: 37569047 PMCID: PMC10418954 DOI: 10.3390/ijerph20156507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition characterized by persistent inflammation in the airways, resulting in narrowing and obstruction of the air passages. The development of COPD is primarily attributed to long-term exposure to irritants, such as cigarette smoke and environmental pollutants. Among individuals hospitalized for exacerbations of COPD, approximately one in five is readmitted within 30 days of discharge or encounters immediate post-discharge complications, highlighting a lack of adequate preparedness for self-management. To address this inadequate preparedness, transitional care services (TCS) have emerged as a promising approach. Therefore, this study primarily aims to present a detailed protocol for a multi-site, single-blind, randomized, controlled trial (RCT) aimed at enhancing self-management competency and overall quality of life for patients with COPD through the provision of TCS, facilitated by a proficient Clinical Research Coordinator. The RCT intervention commenced in September 2022 and is set to conclude in December 2024, with a total of 362 COPD patients anticipated to be enrolled in the study. The intervention program encompasses various components, including an initial assessment during hospitalization, comprehensive self-management education, facilitation of social welfare connections, post-discharge home visits, and regular telephone monitoring. Furthermore, follow-up evaluations are conducted at both one month and three months after discharge to assess the effectiveness of the intervention in terms of preventing re-hospitalization, reducing acute exacerbations, and enhancing disease awareness among participants. The results of this study are expected to provide a basis for the development of TCS fee payment policies for future health insurance.
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Affiliation(s)
- Heui-Sug Jo
- Department of Health Policy and Management, School of Medicine, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea; (H.-S.J.)
| | - Woo-Jin Kim
- Department of Internal Medicine, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea;
| | - Yukyung Park
- Department of Preventive Medicine, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon-si 24289, Republic of Korea;
| | - Yu-Seong Hwang
- Department of Health Policy and Management, School of Medicine, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea; (H.-S.J.)
| | - Seon-Sook Han
- Department of Internal Medicine, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon-si 24289, Republic of Korea; (S.-S.H.); (Y.-J.H.); (D.M.)
| | - Yeon-Jeong Heo
- Department of Internal Medicine, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon-si 24289, Republic of Korea; (S.-S.H.); (Y.-J.H.); (D.M.)
| | - Dahye Moon
- Department of Internal Medicine, Kangwon National University Hospital, 156, Baengnyeong-ro, Chuncheon-si 24289, Republic of Korea; (S.-S.H.); (Y.-J.H.); (D.M.)
| | - Su-Kyoung Kim
- Department of Health Policy and Management, School of Medicine, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea; (H.-S.J.)
| | - Chang-Youl Lee
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, 77, Sakju-ro, Chuncheon-si 24253, Republic of Korea
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Frid S, Pastor Duran X, Bracons Cucó G, Pedrera-Jiménez M, Serrano-Balazote P, Muñoz Carrero A, Lozano-Rubí R. An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology. JMIR Med Inform 2023; 11:e44547. [PMID: 36884279 PMCID: PMC10034609 DOI: 10.2196/44547] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/28/2022] [Accepted: 01/05/2023] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. OBJECTIVE The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. METHODS First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. RESULTS Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. CONCLUSIONS This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.
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Affiliation(s)
- Santiago Frid
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
| | - Xavier Pastor Duran
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
| | | | | | | | - Adolfo Muñoz Carrero
- Unit of Investigation in Telemedicine and Digital Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Raimundo Lozano-Rubí
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
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Chakraborty S, Mallick I, Bhattacharyya T, Moses A, Achari RB, Chatterjee S. State of use of Electronic Data Capture (EDC) tools in randomized controlled trials in India. HEALTH POLICY AND TECHNOLOGY 2022. [DOI: 10.1016/j.hlpt.2022.100662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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Modly LA, Smith DJ. The need for data management standards in public health nursing: A narrative review and case study. Public Health Nurs 2022; 39:1027-1033. [PMID: 35263460 DOI: 10.1111/phn.13066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/09/2022] [Accepted: 02/15/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Data management is the key to the success of all projects and research. The ability to safely store, manipulate, and decipher data in real time is invaluable. Currently data management standards in public health are non-existent. Since the invention of computers real-time data retrieval and analysis has been possible but underutilized by researchers in the field. Historically, most small research studies and field-based projects have utilized spreadsheets for data management, which often proves problematic as the project grows. However, a viable and superior alternative exists in relational databases, such as REDCap. Relational databases allow for easier concatenation of multiple legacy datasets, facilitate data entry with surveys that incorporate branching logic, and allow for real time data entry in the field without the need for WIFI. METHODS One example of a public health project being transitioned from spreadsheet data management to a relational database is the Farmworker Family Health Program based out of the Lillian Carter Center for Global Health & Social Responsibility at Emory University's Nell Hodgson Woodruff School of Nursing. The data management transition from spreadsheets to REDCap has provided the team with unique insight into the data that has been collected in the 30 years the program has been running. CONCLUSION Through this case study, we identify the need for and recommend that those in public health nursing utilize relational databases when collecting data during research studies or as electronic medical records for field clinics.
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Affiliation(s)
- Lori A Modly
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
| | - Daniel J Smith
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia.,Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia
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Maré IA, Kramer B, Hazelhurst S, Nhlapho MD, Zent R, Harris PA, Klipin M. Electronic Data Capture System (REDCap) for Health Care Research and Training in a Resource-Constrained Environment: Technology Adoption Case Study. JMIR Med Inform 2022; 10:e33402. [PMID: 36040763 PMCID: PMC9472062 DOI: 10.2196/33402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 03/01/2022] [Accepted: 05/31/2022] [Indexed: 01/04/2023] Open
Abstract
Background Electronic data capture (EDC) in academic health care organizations provides an opportunity for the management, aggregation, and secondary use of research and clinical data. It is especially important in resource-constrained environments such as the South African public health care sector, where paper records are still the main form of clinical record keeping. Objective The aim of this study was to describe the strategies followed by the University of the Witwatersrand Faculty of Health Sciences (Wits FHS) during the period from 2013 to 2021 to overcome resistance to, and encourage the adoption of, the REDCap (Research Electronic Data Capture; Vanderbilt University) system by academic and clinical staff. REDCap has found wide use in varying domains, including clinical studies and research projects as well as administrative, financial, and human resource applications. Given REDCap’s global footprint in >5000 institutions worldwide and potential for future growth, the strategies followed by the Wits FHS to support users and encourage adoption may be of importance to others using the system, particularly in resource-constrained settings. Methods The strategies to support users and encourage adoption included top-down organizational support; secure and reliable application, hosting infrastructure, and systems administration; an enabling and accessible REDCap support team; regular hands-on training workshops covering REDCap project setup and data collection instrument design techniques; annual local symposia to promote networking and awareness of all the latest software features and best practices for using them; participation in REDCap Consortium activities; and regular and ongoing mentorship from members of the Vanderbilt University Medical Center. Results During the period from 2013 to 2021, the use of the REDCap EDC system by individuals at the Wits FHS increased, respectively, from 129 active user accounts to 3447 active user accounts. The number of REDCap projects increased from 149 in 2013 to 12,865 in 2021. REDCap at Wits also supported various publications and research outputs, including journal articles and postgraduate monographs. As of 2020, a total of 233 journal articles and 87 postgraduate monographs acknowledged the use of the Wits REDCap system. Conclusions By providing reliable infrastructure and accessible support resources, we were able to successfully implement and grow the REDCap EDC system at the Wits FHS and its associated academic medical centers. We believe that the increase in the use of REDCap was driven by offering a dependable, secure service with a strong end-user training and support model. This model may be applied by other academic and health care organizations in resource-constrained environments planning to implement EDC technology.
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Affiliation(s)
- Irma Adele Maré
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa
| | - Beverley Kramer
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Mapule Dorcus Nhlapho
- Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Roy Zent
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul A Harris
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Klipin
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa
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Improving Privacy-preserving Healthcare Data Sharing in a Cloud Environment Using Hybrid Encryption. ACTA INFORMATICA PRAGENSIA 2022. [DOI: 10.18267/j.aip.182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Pemmaraju R, Minahan R, Wang E, Schadl K, Daldrup-Link H, Habte F. Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT). Tomography 2022; 8:1453-1462. [PMID: 35736865 PMCID: PMC9228304 DOI: 10.3390/tomography8030117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.
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Affiliation(s)
- Rahul Pemmaraju
- School of Bioengineering and Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Robert Minahan
- Computational and Systems Biology, University of California-Los Angeles, Los Angeles, CA 90095, USA;
| | - Elise Wang
- School of Medicine, University of Rochester, Rochester, NY 14642, USA;
| | - Kornel Schadl
- Department of Orthopedic Surgery, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Heike Daldrup-Link
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Frezghi Habte
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
- Correspondence:
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Driver S, Gray S, Sikhondze W, Awuonda K, Wilcox H, Segrt A, Pandya L, Roth J, Makanga M, Lang T. The European & Developing Countries Clinical Trials Partnership (EDCTP) Knowledge Hub: developing an open platform for facilitating high-quality clinical research. Trials 2022; 23:374. [PMID: 35526046 PMCID: PMC9077850 DOI: 10.1186/s13063-022-06311-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
There is stark global inequity in health research in terms of where studies happen, who leads the research and the ultimate beneficiaries of the results generated. Despite significant efforts made, limited research ideas are conceptualised and implemented in low-resource settings to tackle diseases of poverty, and this is especially true in sub-Saharan Africa. There is strong evidence to show that the barriers to locally led research do not vary largely between disease, study type and location and can be largely solved by addressing these common gaps. The European & Developing Countries Clinical Trials Partnership (EDCTP) was established in 2003 as a European response to the global health crisis caused by the three main poverty-related diseases HIV, tuberculosis and malaria. EDCTP has established a model of long-term sustainable capacity development integrated into clinical trials which addresses this lack of locally led research in sub-Saharan Africa, supporting the development of individual and institutional capacity and research outputs that change the management, prevention and treatment of poverty-related and neglected infectious diseases across Africa. In recognition of emergent data on what the barriers and enablers are to long-term, sustainable capabilities to run studies, EDCTP formed a new collaboration with The Global Health Network (TGHN) in September 2017, with the aim to make a set of cross-cutting tools and resources to support the planning, writing and delivery of high-quality clinical trials available to research staff wherever they are in the world, especially those in low- and middle-income countries (LMICs) via TGHN platform. These new resources developed on the ‘EDCTP Knowledge Hub’ are those identified in the mixed method study described in this commentary as being key to addressing the gaps that the research community report as the most limiting elements in their ability to design and implement studies. The Knowledge Hub aims to make these tools freely available to any potential health research team in need of support and guidance in designing and running their own studies, particularly in low-resource settings. The purpose is to provide open access to the specific guidance, information and tools these teams cannot otherwise access freely. Ultimately, this will enable them to design and lead their own high-quality studies addressing local priorities with global alignment, generating new data that can change health outcomes in their communities.
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Affiliation(s)
- Samuel Driver
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK.
| | - Shan Gray
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK
| | - Welile Sikhondze
- National TB Control Program, Ministry of Health, Mbabane, Eswatini
| | - Ken Awuonda
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK
| | - Helena Wilcox
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK
| | - Alexis Segrt
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK
| | - Lara Pandya
- European & Developing Countries Clinical Trials Partnership, The Hague, The Netherlands
| | - Johanna Roth
- European & Developing Countries Clinical Trials Partnership, The Hague, The Netherlands
| | - Michael Makanga
- European & Developing Countries Clinical Trials Partnership, The Hague, The Netherlands
| | - Trudie Lang
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK
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Youn J, Rai N, Tagkopoulos I. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nat Commun 2022; 13:2360. [PMID: 35487919 PMCID: PMC9055065 DOI: 10.1038/s41467-022-29993-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/04/2022] [Indexed: 11/09/2022] Open
Abstract
We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.
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Affiliation(s)
- Jason Youn
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Navneet Rai
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, CA, 95616, USA.
- Genome Center, University of California, Davis, CA, 95616, USA.
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA.
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Syn SY, Kim S. Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level. Health Info Libr J 2022; 39:347-356. [PMID: 35472824 DOI: 10.1111/hir.12433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. OBJECTIVES This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. METHODS Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. RESULTS The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. DISCUSSION Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. CONCLUSION Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.
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Affiliation(s)
- Sue Yeon Syn
- Department of Library and Information Science, The Catholic University of America, Washington, District of Columbia, USA
| | - Soojung Kim
- Department of Library and Information, Science, Jeonbuk National University, Jeonju, South Korea
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12
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Donner EK. Research data management systems and the organization of universities and research institutes: A systematic literature review. JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE 2022. [DOI: 10.1177/09610006211070282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.
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13
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Raboudi A, Allanic M, Balvay D, Hervé PY, Viel T, Yoganathan T, Certain A, Hilbey J, Charlet J, Durupt A, Boutinaud P, Eynard B, Tavitian B. The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology. J Biomed Inform 2022; 127:104007. [DOI: 10.1016/j.jbi.2022.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/24/2021] [Accepted: 01/28/2022] [Indexed: 11/16/2022]
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14
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Van Bulck L, Wampers M, Moons P. Research Electronic Data Capture (REDCap): tackling data collection, management, storage, and privacy challenges. Eur J Cardiovasc Nurs 2021; 21:85-91. [PMID: 34741600 DOI: 10.1093/eurjcn/zvab104] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 10/20/2021] [Indexed: 11/15/2022]
Abstract
Data are the basis of research; without data, there is no research. However, growing internationalization of research, increased complexity of study designs, and stricter legislation make high-quality data collection, management, and storage more important, but also more challenging than ever. This article provides an overview of common challenges clinical researchers face when collecting, managing, and storing data and how REDCap, Research Electronic Data Capture, can be a possible solution to address these challenges.
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Affiliation(s)
- Liesbet Van Bulck
- KU Leuven Department of Public Health and Primary Care, KU Leuven - University of Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium.,Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium
| | - Martien Wampers
- University Psychiatric Center, University Hospitals Leuven, Leuvensesteenweg 517, 3070 Kortenberg, Belgium
| | - Philip Moons
- KU Leuven Department of Public Health and Primary Care, KU Leuven - University of Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium.,Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens backe 1, 413 46 Gothenburg, Sweden.,Department of Paediatrics and Child Health, University of Cape Town, Klipfontein Rd, Rondebosch, 7700 Cape Town, South Africa
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15
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Reichmann S, Klebel T, Hasani‐Mavriqi I, Ross‐Hellauer T. Between administration and research: Understanding data management practices in an institutional context. J Assoc Inf Sci Technol 2021. [DOI: 10.1002/asi.24492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Stefan Reichmann
- Open and Reproducible Research Group, Institute of Interactive Systems and Data Science Graz University of Technology Graz 8010 Austria
| | | | - Ilire Hasani‐Mavriqi
- RDM Team, Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria
| | - Tony Ross‐Hellauer
- Open and Reproducible Research Group, Institute of Interactive Systems and Data Science Graz University of Technology Graz 8010 Austria
- Know Center GmbH Graz 8010 Austria
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16
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Fadlelmola FM, Zass L, Chaouch M, Samtal C, Ras V, Kumuthini J, Panji S, Mulder N. Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations. J Biomed Inform 2021; 122:103900. [PMID: 34506960 PMCID: PMC9123155 DOI: 10.1016/j.jbi.2021.103900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/24/2021] [Accepted: 08/25/2021] [Indexed: 11/25/2022]
Abstract
Drafting and writing a data management plan (DMP) is increasingly seen as a key part of the academic research process. A DMP is a document that describes how a researcher will collect, document, describe, share, and preserve the data that will be generated as part of a research project. The DMP illustrates the importance of utilizing best practices through all stages of working with data while ensuring accessibility, quality, and longevity of the data. The benefits of writing a DMP include compliance with funder and institutional mandates; making research more transparent (for reproduction and validation purposes); and FAIR (findable, accessible, interoperable, reusable); protecting data subjects and compliance with the General Data Protection Regulation (GDPR) and/or local data protection policies. In this review, we highlight the importance of a DMP in modern biomedical research, explaining both the rationale and current best practices associated with DMPs. In addition, we outline various funders’ requirements concerning DMPs and discuss open-source tools that facilitate the development and implementation of a DMP. Finally, we discuss DMPs in the context of African research, and the considerations that need to be made in this regard.
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Affiliation(s)
- Faisal M Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Al-Gamaa Ave, Khartoum 11115, Sudan.
| | - Lyndon Zass
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Melek Chaouch
- Laboratory of Bioinformatics Biomathematics and Biostatistics (LR16IPT09), Institut Pasteur de Tunis, 13 Place Pasteur, B.P. 74 1002 Tunis, Belvédère, Tunisia
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-food and Health, Faculty of Sciences Dhar El Mahraz-Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco
| | - Verena Ras
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Judit Kumuthini
- South African Bioinformatics Institute (SANBI), University of Western Cape (UWC), Life Sciences Building, Bellville, Cape Town, South Africa
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI Africa Wellcome Trust Centre, University of Cape Town, South Africa
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17
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Hussain MI, Figueiredo MC, Tran BD, Su Z, Molldrem S, Eikey EV, Chen Y. A scoping review of qualitative research in JAMIA: past contributions and opportunities for future work. J Am Med Inform Assoc 2021; 28:402-413. [PMID: 33225361 DOI: 10.1093/jamia/ocaa179] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 08/07/2020] [Accepted: 07/17/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE Qualitative methods are particularly well-suited to studying the complexities and contingencies that emerge in the development, preparation, and implementation of technological interventions in real-world clinical practice, and much remains to be done to use these methods to their full advantage. We aimed to analyze how qualitative methods have been used in health informatics research, focusing on objectives, populations studied, data collection, analysis methods, and fields of analytical origin. METHODS We conducted a scoping review of original, qualitative empirical research in JAMIA from its inception in 1994 to 2019. We queried PubMed to identify relevant articles, ultimately including and extracting data from 158 articles. RESULTS The proportion of qualitative studies increased over time, constituting 4.2% of articles published in JAMIA overall. Studies overwhelmingly used interviews, observations, grounded theory, and thematic analysis. These articles used qualitative methods to analyze health informatics systems before, after, and separate from deployment. Providers have typically been the main focus of studies, but there has been an upward trend of articles focusing on healthcare consumers. DISCUSSION While there has been a rich tradition of qualitative inquiry in JAMIA, its scope has been limited when compared with the range of qualitative methods used in other technology-oriented fields, such as human-computer interaction, computer-supported cooperative work, and science and technology studies. CONCLUSION We recommend increased public funding for and adoption of a broader variety of qualitative methods by scholars, practitioners, and policy makers and an expansion of the variety of participants studied. This should lead to systems that are more responsive to practical needs, improving usability, safety, and outcomes.
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Affiliation(s)
- Mustafa I Hussain
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California Irvine, Irvine, California, USA
| | - Mayara Costa Figueiredo
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California Irvine, Irvine, California, USA
| | - Brian D Tran
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California Irvine, Irvine, California, USA.,Medical Scientist Training Program, School of Medicine, University of California Irvine, Irvine, California, USA
| | - Zhaoyuan Su
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California Irvine, Irvine, California, USA
| | - Stephen Molldrem
- Department of Anthropology, University of California Irvine, Irvine, California, USA
| | - Elizabeth V Eikey
- Department of Family Medicine and Public Health & Design Lab, University of California San Diego, San Diego, California, USA
| | - Yunan Chen
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California Irvine, Irvine, California, USA
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18
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Hamdi Y, Zass L, Othman H, Radouani F, Allali I, Hanachi M, Okeke CJ, Chaouch M, Tendwa MB, Samtal C, Mohamed Sallam R, Alsayed N, Turkson M, Ahmed S, Benkahla A, Romdhane L, Souiai O, Tastan Bishop Ö, Ghedira K, Mohamed Fadlelmola F, Mulder N, Kamal Kassim S. Human OMICs and Computational Biology Research in Africa: Current Challenges and Prospects. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 25:213-233. [PMID: 33794662 PMCID: PMC8060717 DOI: 10.1089/omi.2021.0004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Following the publication of the first human genome, OMICs research, including genomics, transcriptomics, proteomics, and metagenomics, has been on the rise. OMICs studies revealed the complex genetic diversity among human populations and challenged our understandings of genotype-phenotype correlations. Africa, being the cradle of the first modern humans, is distinguished by a large genetic diversity within its populations and rich ethnolinguistic history. However, the available human OMICs tools and databases are not representative of this diversity, therefore creating significant gaps in biomedical research. African scientists, students, and publics are among the key contributors to OMICs systems science. This expert review examines the pressing issues in human OMICs research, education, and development in Africa, as seen through a lens of computational biology, public health relevant technology innovation, critically-informed science governance, and how best to harness OMICs data to benefit health and societies in Africa and beyond. We underscore the disparities between North and Sub-Saharan Africa at different levels. A harmonized African ethnolinguistic classification would help address annotation challenges associated with population diversity. Finally, building on the existing strategic research initiatives, such as the H3Africa and H3ABioNet Consortia, we highly recommend addressing large-scale multidisciplinary research challenges, strengthening research collaborations and knowledge transfer, and enhancing the ability of African researchers to influence and shape national and international research, policy, and funding agendas. This article and analysis contribute to a deeper understanding of past and current challenges in the African OMICs innovation ecosystem, while also offering foresight on future innovation trajectories.
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Affiliation(s)
- Yosr Hamdi
- Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
- Laboratory of Human and Experimental Pathology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Lyndon Zass
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Houcemeddine Othman
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Fouzia Radouani
- Chlamydiae and Mycoplasmas Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, and Genomic Center of Human Pathologies, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
| | - Mariem Hanachi
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
- Faculty of Science of Bizerte, Zarzouna, University of Carthage, Tunis, Tunisia
| | - Chiamaka Jessica Okeke
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Melek Chaouch
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
| | - Maureen Bilinga Tendwa
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-food and Health, Faculty of Sciences Dhar El Mahraz–Sidi Mohammed Ben Abdellah University, Fez, Morocco
- University of Mohamed Premier, Oujda, Morocco
| | - Reem Mohamed Sallam
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
- Department of Basic Medical Sciences, Faculty of Medicine, Galala University, Suez, Egypt
| | - Nihad Alsayed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Michael Turkson
- The National Institute for Mathematical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Samah Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
| | - Lilia Romdhane
- Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
- Faculty of Science of Bizerte, Zarzouna, University of Carthage, Tunis, Tunisia
| | - Oussema Souiai
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institut Pasteur de Tunis, Université Tunis El Manar, Tunis, Tunisia
| | - Faisal Mohamed Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Samar Kamal Kassim
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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19
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Vignolo SM, Diray-Arce J, McEnaney K, Rao S, Shannon CP, Idoko OT, Cole F, Darboe A, Cessay F, Ben-Othman R, Tebbutt SJ, Kampmann B, Levy O, Ozonoff A. A cloud-based bioinformatic analytic infrastructure and Data Management Core for the Expanded Program on Immunization Consortium. J Clin Transl Sci 2020; 5:e52. [PMID: 33948273 PMCID: PMC8057481 DOI: 10.1017/cts.2020.546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 08/06/2020] [Accepted: 09/14/2020] [Indexed: 12/30/2022] Open
Abstract
The Expanded Program for Immunization Consortium - Human Immunology Project Consortium study aims to employ systems biology to identify and characterize vaccine-induced biomarkers that predict immunogenicity in newborns. Key to this effort is the establishment of the Data Management Core (DMC) to provide reliable data and bioinformatic infrastructure for centralized curation, storage, and analysis of multiple de-identified "omic" datasets. The DMC established a cloud-based architecture using Amazon Web Services to track, store, and share data according to National Institutes of Health standards. The DMC tracks biological samples during collection, shipping, and processing while capturing sample metadata and associated clinical data. Multi-omic datasets are stored in access-controlled Amazon Simple Storage Service (S3) for data security and file version control. All data undergo quality control processes at the generating site followed by DMC validation for quality assurance. The DMC maintains a controlled computing environment for data analysis and integration. Upon publication, the DMC deposits finalized datasets to public repositories. The DMC architecture provides resources and scientific expertise to accelerate translational discovery. Robust operations allow rapid sharing of results across the project team. Maintenance of data quality standards and public data deposition will further benefit the scientific community.
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Affiliation(s)
- Sofia M. Vignolo
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Division of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
| | - Joann Diray-Arce
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Division of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Kerry McEnaney
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
| | - Shun Rao
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
| | | | - Olubukola T. Idoko
- Vaccines & Immunity Theme, Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia
- Vaccine Centre, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Fatoumata Cole
- Vaccines & Immunity Theme, Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia
| | - Alansana Darboe
- Vaccines & Immunity Theme, Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia
- Vaccine Centre, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Fatoumatta Cessay
- Vaccines & Immunity Theme, Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia
| | | | - Scott J. Tebbutt
- PROOF Centre of Excellence, Vancouver, BC, Canada
- Centre for Heart Lung Innovation, St Paul’s Hospital, University of British Columbia, Vancouver, BC, Canada
- Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Beate Kampmann
- Vaccines & Immunity Theme, Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Banjul, The Gambia
- Vaccine Centre, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Ofer Levy
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Division of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Al Ozonoff
- Precision Vaccines Program, Boston Children’s Hospital, Boston, MA, USA
- Division of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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20
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Houston L, Yu P, Martin A, Probst Y. Heterogeneity in clinical research data quality monitoring: A national survey. J Biomed Inform 2020; 108:103491. [DOI: 10.1016/j.jbi.2020.103491] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/19/2020] [Accepted: 06/16/2020] [Indexed: 01/21/2023]
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21
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McKenzie KA, Hunt SL, Hulshof G, Mudaranthakam DP, Meyer K, Vidoni ED, Burns JM, Mahnken JD. A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry. JAMIA Open 2019; 2:516-520. [PMID: 32025648 PMCID: PMC6993996 DOI: 10.1093/jamiaopen/ooz032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/21/2019] [Accepted: 07/22/2019] [Indexed: 12/22/2022] Open
Abstract
Objective Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer’s Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer’s Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.
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Affiliation(s)
- Katelyn A McKenzie
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Suzanne L Hunt
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA.,University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA
| | - Genevieve Hulshof
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Dinesh Pal Mudaranthakam
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA.,University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA
| | - Kayla Meyer
- University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA
| | - Eric D Vidoni
- University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.,Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jeffrey M Burns
- University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.,Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jonathan D Mahnken
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA.,University of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA
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22
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Abstract
We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.
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23
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Krahe MA, Toohey J, Wolski M, Scuffham PA, Reilly S. Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia. HEALTH INF MANAG J 2019; 49:108-116. [DOI: 10.1177/1833358319831318] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. Objective: This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. Method: A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Results: Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Conclusion: Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. Implications: This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.
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Affiliation(s)
| | - Julie Toohey
- Library and Learning Services, Griffith University, Gold Coast, QLD, Australia
| | - Malcolm Wolski
- eResearch Services, Griffith University, Nathan, QLD, Australia
| | - Paul A Scuffham
- Centre for Applied Health Economics, Griffith University, Nathan, QLD, Australia
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Sheena Reilly
- Health Group, Griffith University, Gold Coast, QLD, Australia
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Read KB. Adapting data management education to support clinical research projects in an academic medical center. J Med Libr Assoc 2019; 107:89-97. [PMID: 30598653 PMCID: PMC6300223 DOI: 10.5195/jmla.2019.580] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 09/01/2018] [Indexed: 02/07/2023] Open
Abstract
Background Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown. Case Presentation Leveraging my academic library's experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library's existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop. Conclusions The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.
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Affiliation(s)
- Kevin B Read
- Data Services Librarian and Data Discovery Lead, NYU Health Sciences Library, New York University School of Medicine, 577 First Avenue, New York, NY 10016,
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Ali O, Shrestha A, Soar J, Wamba SF. Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2018. [DOI: 10.1016/j.ijinfomgt.2018.07.009] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Bialke M, Rau H, Thamm OC, Schuldt R, Penndorf P, Blumentritt A, Gött R, Piegsa J, Bahls T, Hoffmann W. Toolbox for Research, or how to facilitate a central data management in small-scale research projects. J Transl Med 2018; 16:16. [PMID: 29370861 PMCID: PMC5785842 DOI: 10.1186/s12967-018-1390-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 01/15/2018] [Indexed: 11/21/2022] Open
Abstract
Background In most research projects budget, staff and IT infrastructures are limiting resources. Especially for small-scale registries and cohort studies professional IT support and commercial electronic data capture systems are too expensive. Consequently, these projects use simple local approaches (e.g. Excel) for data capture instead of a central data management including web-based data capture and proper research databases. This leads to manual processes to merge, analyze and, if possible, pseudonymize research data of different study sites. Results To support multi-site data capture, storage and analyses in small-scall research projects, corresponding requirements were analyzed within the MOSAIC project. Based on the identified requirements, the Toolbox for Research was developed as a flexible software solution for various research scenarios. Additionally, the Toolbox facilitates data integration of research data as well as metadata by performing necessary procedures automatically. Also, Toolbox modules allow the integration of device data. Moreover, separation of personally identifiable information and medical data by using only pseudonyms for storing medical data ensures the compliance to data protection regulations. This pseudonymized data can then be exported in SPSS format in order to enable scientists to prepare reports and analyses. Conclusions The Toolbox for Research was successfully piloted in the German Burn Registry in 2016 facilitating the documentation of 4350 burn cases at 54 study sites. The Toolbox for Research can be downloaded free of charge from the project website and automatically installed due to the use of Docker technology.
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Affiliation(s)
- Martin Bialke
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany.
| | - Henriette Rau
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Oliver C Thamm
- Klinik für Plastische und Ästhetische Chirurgie, Sana-Krankenhaus Gerresheim, Gräulinger Straße 120, 40625, Düsseldorf, Germany
| | - Ronny Schuldt
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Peter Penndorf
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Arne Blumentritt
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Robert Gött
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Jens Piegsa
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Thomas Bahls
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
| | - Wolfgang Hoffmann
- Department Epidemiology of Health Care and Community Health, Institute for Community Medicine, University Medicine Greifswald, Ellernholzstr. 1-2, 17487, Greifswald, Germany
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Chen X, Wu M. Survey on the Needs for Chemistry Research Data Management and Sharing. JOURNAL OF ACADEMIC LIBRARIANSHIP 2017. [DOI: 10.1016/j.acalib.2017.06.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Foran DJ, Chen W, Chu H, Sadimin E, Loh D, Riedlinger G, Goodell LA, Ganesan S, Hirshfield K, Rodriguez L, DiPaola RS. Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology. Cancer Inform 2017; 16:1176935117694349. [PMID: 28469389 PMCID: PMC5392017 DOI: 10.1177/1176935117694349] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Accepted: 01/26/2017] [Indexed: 11/16/2022] Open
Abstract
Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.
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Affiliation(s)
- David J Foran
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Wenjin Chen
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Huiqi Chu
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Evita Sadimin
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Doreen Loh
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Gregory Riedlinger
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Lauri A Goodell
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Kim Hirshfield
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Lorna Rodriguez
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
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Neves Tafula SM, Moreira da Silva N, Rozanski VE, Silva Cunha JP. ABrIL - Advanced Brain Imaging Lab : a cloud based computation environment for cooperative neuroimaging projects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2014:534-7. [PMID: 25570014 DOI: 10.1109/embc.2014.6943646] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.
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Myneni S, Patel VL, Bova GS, Wang J, Ackerman CF, Berlinicke CA, Chen SH, Lindvall M, Zack DJ. Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 126:160-70. [PMID: 26652980 PMCID: PMC4778387 DOI: 10.1016/j.cmpb.2015.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 10/21/2015] [Accepted: 11/03/2015] [Indexed: 06/05/2023]
Abstract
This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity.
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Affiliation(s)
- Sahiti Myneni
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.
| | - Vimla L Patel
- New York Academy of Medicine, New York, NY, United States; Department of Biomedical Informatics, Arizona State University, United States
| | - G Steven Bova
- Departments of Pathology, Genetic Medicine, Health Sciences Informatics, Oncology, and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jian Wang
- BioFortis Inc., Columbia, MD, United States
| | - Christopher F Ackerman
- Fraunhofer Institute for Experimental Software Engineering, College Park, MD, United States
| | | | | | - Mikael Lindvall
- Fraunhofer Institute for Experimental Software Engineering, College Park, MD, United States
| | - Donald J Zack
- Departments of Pathology, Genetic Medicine, Health Sciences Informatics, Oncology, and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Wilmer Eye Institute, United States; Institute of Genetic Medicine Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Johnson SB, Farach FJ, Pelphrey K, Rozenblit L. Data management in clinical research: Synthesizing stakeholder perspectives. J Biomed Inform 2016; 60:286-93. [PMID: 26925516 DOI: 10.1016/j.jbi.2016.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study assesses data management needs in clinical research from the perspectives of researchers, software analysts and developers. MATERIALS AND METHODS This is a mixed-methods study that employs sublanguage analysis in an innovative manner to link the assessments. We performed content analysis using sublanguage theory on transcribed interviews conducted with researchers at four universities. A business analyst independently extracted potential software features from the transcriptions, which were translated into the sublanguage. This common sublanguage was then used to create survey questions for researchers, analysts and developers about the desirability and difficulty of features. Results were synthesized using the common sublanguage to compare stakeholder perceptions with the original content analysis. RESULTS Individual researchers exhibited significant diversity of perspectives that did not correlate by role or site. Researchers had mixed feelings about their technologies, and sought improvements in integration, interoperability and interaction as well as engaging with study participants. Researchers and analysts agreed that data integration has higher desirability and mobile technology has lower desirability but disagreed on the desirability of data validation rules. Developers agreed that data integration and validation are the most difficult to implement. DISCUSSION Researchers perceive tasks related to study execution, analysis and quality control as highly strategic, in contrast with tactical tasks related to data manipulation. Researchers have only partial technologic support for analysis and quality control, and poor support for study execution. CONCLUSION Software for data integration and validation appears critical to support clinical research, but may be expensive to implement. Features to support study workflow, collaboration and engagement have been underappreciated, but may prove to be easy successes. Software developers should consider the strategic goals of researchers with regard to the overall coordination of research projects and teams, workflow connecting data collection with analysis and processes for improving data quality.
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Affiliation(s)
- Stephen B Johnson
- Division of Health Informatics, Weill Cornell Medical College, 425 East 61st Street, DV-317, New York, NY 10065, United States.
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Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Inform 2016; 3:133-143. [PMID: 27747590 PMCID: PMC4999567 DOI: 10.1007/s40708-016-0038-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Accepted: 02/03/2016] [Indexed: 12/02/2022] Open
Abstract
Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.
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Knowledge Discovery from Complex High Dimensional Data. SOLVING LARGE SCALE LEARNING TASKS. CHALLENGES AND ALGORITHMS 2016. [DOI: 10.1007/978-3-319-41706-6_7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Afgan E, Sloggett C, Goonasekera N, Makunin I, Benson D, Crowe M, Gladman S, Kowsar Y, Pheasant M, Horst R, Lonie A. Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud. PLoS One 2015; 10:e0140829. [PMID: 26501966 PMCID: PMC4621043 DOI: 10.1371/journal.pone.0140829] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 09/29/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. RESULTS We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. CONCLUSIONS This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.
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Affiliation(s)
- Enis Afgan
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Centre for Computing and Informatics (CIR), Rudjer Boskovic Institute (RBI), Zagreb, Croatia
| | - Clare Sloggett
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
| | - Nuwan Goonasekera
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
| | - Igor Makunin
- Research Computing Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Derek Benson
- Research Computing Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Mark Crowe
- Queensland Facility for Advanced Bioinformatics (QFAB), University of Queensland, Brisbane, Queensland, Australia
| | - Simon Gladman
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
| | - Yousef Kowsar
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
| | - Michael Pheasant
- Research Computing Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Ron Horst
- Research Computing Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Andrew Lonie
- Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne, Melbourne, Victoria, Australia
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Black EF, Marini L, Vaidya A, Berman D, Willman M, Salomon D, Bartholomew A, Kenyon N, McHenry K. Using Hidden Markov Models to Determine Changes in Subject Data over Time, Studying the Immunoregulatory effect of Mesenchymal Stem Cells. PROCEEDINGS ... IEEE INTERNATIONAL CONFERENCE ON ESCIENCE. IEEE INTERNATIONAL CONFERENCE ON ESCIENCE 2015; 1:83-91. [PMID: 26075290 DOI: 10.1109/escience.2014.29] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A novel application of Hidden Markov Models is used to help research intended to test the immunuregulatory effects of mesenchymal stem cells in a cynomolgus monkey model of islet transplantation. The Hidden Markov Model, an unsupervised learning data mining technique, is used to automatically determine the postoperative day (POD) corresponding to a decrease of graft function, a possible sign of transplant rejection, on nonhuman primates after isolated islet cell transplant. Currently, decrease of graft function is being determined solely on experts' judgment. Further, information gathered from the evaluation of construted Hidden Markov Models is used as part of a clustering method to aggregate the nonhuman subjects into groups or clusters with the objective of finding similarities that could potentially help predict the health outcome of subjects undergoing postoperative care. Results on expert labeled data show the HMM to be accurate 60% of the time. Clusters based on the HMMs further suggest a possible correspondence between donor haplotypes matching and loss of function outcomes.
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Affiliation(s)
- Edgar F Black
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Luigi Marini
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Ashwini Vaidya
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Dora Berman
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Melissa Willman
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Dan Salomon
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Amelia Bartholomew
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Norma Kenyon
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
| | - Kenton McHenry
- National Center for Supercomputing Applications University of Illinois at Urbana-Champaign
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Bhavani SR, Senthilkumar J, Chilambuchelvan AG, Manjula D, Krishnamoorthy R, Kannan A. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency. JMIR Med Inform 2015; 3:e12. [PMID: 25830608 PMCID: PMC4393505 DOI: 10.2196/medinform.3709] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 08/30/2014] [Accepted: 01/12/2015] [Indexed: 11/29/2022] Open
Abstract
Background The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. Objective We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called “CIMIDx”, based on representative association rules that support the diagnosis of medical images (mammograms). Methods The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype’s classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user’s perspective. Results We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals and health centers. The CIMIDx prototype achieved high sensitivity of up to 99.29%, and accuracy of up to 98%. The second set of experiments evaluated CIMIDx use for breast health issues, using t tests and Pearson chi-square tests to assess differences, and binary logistic regression to estimate the odds ratio (OR) for the predictors’ use of CIMIDx. For the prototype usage statistics for the same 150 breast cancer survivors, we interviewed 114 (76.0%), through self-report questionnaires from CIMIDx blogs. The frequency of log-ins/person ranged from 0 to 30, total duration/person from 0 to 1500 minutes (25 hours). The 114 participants continued logging in to all phases, resulting in an intervention adherence rate of 44.3% (95% CI 33.2-55.9). The overall performance of the prototype for the good category, reported usefulness of the prototype (P=.77), overall satisfaction of the prototype (P=.31), ease of navigation (P=.89), user friendliness evaluation (P=.31), and overall satisfaction (P=.31). Positive evaluations given by 100 participants via a Web-based questionnaire supported our hypothesis. Conclusions The present study shows that women felt favorably about the use of a generic fully automated cloud-based self- management prototype. The study also demonstrated that the CIMIDx prototype resulted in the detection of more cancers in screening and diagnosing patients, with an increased accuracy rate.
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Affiliation(s)
- Selvaraj Rani Bhavani
- Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai, Tamilnadu, India
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A Domain-Expert Centered Process Model for Knowledge Discovery in Medical Research: Putting the Expert-in-the-Loop. BRAIN INFORMATICS AND HEALTH 2015. [DOI: 10.1007/978-3-319-23344-4_38] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Ngari MM, Waithira N, Chilengi R, Njuguna P, Lang T, Fegan G. Experience of using an open source clinical trials data management software system in Kenya. BMC Res Notes 2014; 7:845. [PMID: 25424974 PMCID: PMC4256812 DOI: 10.1186/1756-0500-7-845] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 11/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Clinical trials data management (CTDM) remains one of the many challenges in running state of the art trials in resource-poor settings since most trials do not allocate, or have available, sufficient resources for CTDM and because of poor internet connectivity. Open-source software like OpenClinica could be a solution in such scenarios. FINDINGS In 2007, the KEMRI-Wellcome Trust Research Programme (KWTRP) adopted OpenClinica (OC) community edition, an open-source software system and we share our experience and lessons learnt since its adoption. We have used OC in three different modes; direct remote data entry from sites through Global System for Mobile Communications (GSM) modems, a centralized data centre approach where all data from paper records were entered at a central location and an off-line approach where data entry was done from a copy of database hosted on a field-site server laptop, then data uploaded to a centralized server later. We have used OC in eleven trials/studies with a cumulative number of participants in excess of 6000. These include large and complex trials, with multiple sites recruiting in different regions of East Africa. In the process, we have developed substantial local capacity through hands-on training and mentorship, which we have now begun to share with other institutions in the region. CONCLUSIONS Our experience demonstrates that an open source data management system to manage trials' data can be utilized to international industry standards in resource-poor countries.
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Affiliation(s)
- Moses M Ngari
- KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medical, Research Coast, PO Box 230, Kilifi 80108, Kenya.
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Lozano-Rubí R, Pastor X, Lozano E. OWLing Clinical Data Repositories With the Ontology Web Language. JMIR Med Inform 2014; 2:e14. [PMID: 25599697 PMCID: PMC4288111 DOI: 10.2196/medinform.3023] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Revised: 02/24/2014] [Accepted: 04/28/2014] [Indexed: 11/23/2022] Open
Abstract
Background The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. Objective The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. Methods We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Results Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. Conclusions OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems.
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Affiliation(s)
- Raimundo Lozano-Rubí
- Hospital Clínic, Unit of Medical Informatics, University of Barcelona, Barcelona, Spain.
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Li M, Chen YB, Clintworth WA. Expanding roles in a library-based bioinformatics service program: a case study. J Med Libr Assoc 2014; 101:303-9. [PMID: 24163602 DOI: 10.3163/1536-5050.101.4.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
QUESTION How can a library-based bioinformatics support program be implemented and expanded to continuously support the growing and changing needs of the research community? SETTING A program at a health sciences library serving a large academic medical center with a strong research focus is described. METHODS The bioinformatics service program was established at the Norris Medical Library in 2005. As part of program development, the library assessed users' bioinformatics needs, acquired additional funds, established and expanded service offerings, and explored additional roles in promoting on-campus collaboration. RESULTS Personnel and software have increased along with the number of registered software users and use of the provided services. CONCLUSION With strategic efforts and persistent advocacy within the broader university environment, library-based bioinformatics service programs can become a key part of an institution's comprehensive solution to researchers' ever-increasing bioinformatics needs.
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Kessel KA, Bohn C, Engelmann U, Oetzel D, Bougatf N, Bendl R, Debus J, Combs SE. Five-year experience with setup and implementation of an integrated database system for clinical documentation and research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:206-217. [PMID: 24629596 DOI: 10.1016/j.cmpb.2014.02.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Revised: 01/30/2014] [Accepted: 02/06/2014] [Indexed: 06/03/2023]
Abstract
In radiation oncology, where treatment concepts are elaborated in interdisciplinary collaborations, handling distributed, large heterogeneous amounts of data efficiently is very important, yet challenging, for an optimal treatment of the patient as well as for research itself. This becomes a strong focus, as we step into the era of modern personalized medicine, relying on various quantitative data information, thus involving the active contribution of multiple medical specialties. Hence, combining patient data from all involved information systems is inevitable for analyses. Therefore, we introduced a documentation and data management system integrated in the clinical environment for electronic data capture. We discuss our concept and five-year experience of a precise electronic documentation system, with special focus on the challenges we encountered. We specify how such a system can be designed and implemented to plan, tailor and conduct (multicenter) clinical trials, ultimately reaching the best clinical performance, and enhancing interdisciplinary and clinical research.
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Affiliation(s)
- Kerstin A Kessel
- Heidelberg University Hospital, Department of Radiation Oncology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
| | - Christian Bohn
- CHILI GmbH, Friedrich-Ebert-Str. 2, 69221 Dossenheim, Germany
| | - Uwe Engelmann
- CHILI GmbH, Friedrich-Ebert-Str. 2, 69221 Dossenheim, Germany
| | - Dieter Oetzel
- Heidelberg University Hospital, Department of Radiation Oncology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Nina Bougatf
- Heidelberg University Hospital, Department of Radiation Oncology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Rolf Bendl
- Heilbronn University, Department of Medical Informatics, Max-Planck-Str. 39, 74081 Heilbronn, Germany
| | - Jürgen Debus
- Heidelberg University Hospital, Department of Radiation Oncology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Stephanie E Combs
- Heidelberg University Hospital, Department of Radiation Oncology, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Technical University of Munich (TUM), Department of Radiation Oncology, Ismaninger Straße 122, Munich, Germany
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Al-Jasmi F, Pramathan T, Swid A, Sahari B, Penefsky HS, Souid AK. Mitochondrial Oxygen Consumption by the Foreskin and its Fibroblast-rich Culture. Sultan Qaboos Univ Med J 2013; 13:411-6. [PMID: 23984027 DOI: 10.12816/0003264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2012] [Revised: 01/02/2013] [Accepted: 03/17/2013] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVES This study investigated the feasibility of using a phosphorescence oxygen analyser to measure cellular respiration (mitochondrial O2 consumption) in foreskin samples and their fibroblast-rich cultures. METHODS Foreskin specimens from normal infants were collected immediately after circumcision and processed for measuring cellular respiration and for culture. Cellular mitochondrial O2 consumption was determined as a function of time from the phosphorescence decay of the Pd (II) meso-tetra-(4-sulfonatophenyl)-tetrabenzoporphyrin. RESULTS In sealed vials containing a foreskin specimen and glucose, O2 concentration decreased linearly with time, confirming the zero-order kinetics of O2 consumption by cytochrome oxidase. Cyanide inhibited O2 consumption, confirming that the oxidation occurred mainly in the mitochondrial respiratory chain. The rate of foreskin respiration (mean ± SD) was 0.074 ± 0.02 μM O2 min(-1) mg(-1) (n = 23). The corresponding rate for fibroblast-rich cultures was 9.84 ± 2.43 μM O2 min(-1) per 10(7) cells (n = 15). Fibroblast respiration was significantly lower in a male infant with dihydrolipoamide dehydrogenase gene mutations, but normalised with the addition of thiamine or carnitine. CONCLUSION The foreskin and its fibroblast-rich culture are suitable for assessment of cellular respiration. However, the clinical utility of foreskin specimens to detect disorders of impaired cellular bioenergetics requires further investigation.
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Affiliation(s)
- Fatma Al-Jasmi
- Department of Paediatrics, College of Medicine & Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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Rodrigues JJPC, de la Torre I, Fernández G, López-Coronado M. Analysis of the security and privacy requirements of cloud-based electronic health records systems. J Med Internet Res 2013; 15:e186. [PMID: 23965254 PMCID: PMC3757992 DOI: 10.2196/jmir.2494] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2012] [Accepted: 06/11/2013] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Cloud Computing paradigm offers eHealth systems the opportunity to enhance the features and functionality that they offer. However, moving patients' medical information to the Cloud implies several risks in terms of the security and privacy of sensitive health records. In this paper, the risks of hosting Electronic Health Records (EHRs) on the servers of third-party Cloud service providers are reviewed. To protect the confidentiality of patient information and facilitate the process, some suggestions for health care providers are made. Moreover, security issues that Cloud service providers should address in their platforms are considered. OBJECTIVE To show that, before moving patient health records to the Cloud, security and privacy concerns must be considered by both health care providers and Cloud service providers. Security requirements of a generic Cloud service provider are analyzed. METHODS To study the latest in Cloud-based computing solutions, bibliographic material was obtained mainly from Medline sources. Furthermore, direct contact was made with several Cloud service providers. RESULTS Some of the security issues that should be considered by both Cloud service providers and their health care customers are role-based access, network security mechanisms, data encryption, digital signatures, and access monitoring. Furthermore, to guarantee the safety of the information and comply with privacy policies, the Cloud service provider must be compliant with various certifications and third-party requirements, such as SAS70 Type II, PCI DSS Level 1, ISO 27001, and the US Federal Information Security Management Act (FISMA). CONCLUSIONS Storing sensitive information such as EHRs in the Cloud means that precautions must be taken to ensure the safety and confidentiality of the data. A relationship built on trust with the Cloud service provider is essential to ensure a transparent process. Cloud service providers must make certain that all security mechanisms are in place to avoid unauthorized access and data breaches. Patients must be kept informed about how their data are being managed.
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Hruby GW, McKiernan J, Bakken S, Weng C. A centralized research data repository enhances retrospective outcomes research capacity: a case report. J Am Med Inform Assoc 2013; 20:563-7. [PMID: 23322812 DOI: 10.1136/amiajnl-2012-001302] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
This paper describes our considerations and methods for implementing an open-source centralized research data repository (CRDR) and reports its impact on retrospective outcomes research capacity in the urology department at Columbia University. We performed retrospective pretest and post-test analyses of user acceptance, workflow efficiency, and publication quantity and quality (measured by journal impact factor) before and after the implementation. The CRDR transformed the research workflow and enabled a new research model. During the pre- and post-test periods, the department's average annual retrospective study publication rate was 11.5 and 25.6, respectively; the average publication impact score was 1.7 and 3.1, respectively. The new model was adopted by 62.5% (5/8) of the clinical scientists within the department. Additionally, four basic science researchers outside the department took advantage of the implemented model. The average proximate time required to complete a retrospective study decreased from 12 months before the implementation to <6 months after the implementation. Implementing a CRDR appears to be effective in enhancing the outcomes research capacity for one academic department.
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Affiliation(s)
- Gregory William Hruby
- Department of Urology, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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Farrell D, Nielsen JE. DataPipeline: automated importing and fitting of large amounts of biophysical data. J Comput Chem 2012; 33:2357-62. [PMID: 22806579 DOI: 10.1002/jcc.23066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2012] [Revised: 06/22/2012] [Accepted: 06/26/2012] [Indexed: 11/07/2022]
Abstract
Raw data from experiments across the biological sciences comes in a large variety of text formats. In small or medium sized laboratories researchers often use an assorted collection of software to interpret, fit, and visualize their data. The spreadsheet is commonly the core component of such a workflow. The limitations of such programs for large amounts of heterogeneous data can be frustrating. We report the construction of DataPipeline, a desktop and command-line application that automates the tasks of importing, fitting, and plotting of text-based data. The software is designed to simplify the process of importing text data from various sources using simple configuration files to describe raw file formats. Once imported, curve fitting can be performed using custom fitting models designed by the user inside the application. Fitted parameters can be grouped together as new datasets to be fitted to other models and experimental uncertainties propagated to give error estimates. This software will be useful for processing of data from high through-put biological experiments or for rapid visualization of pilot data without the need for a chain of different programs to carry out each step. DataPipeline and source code is available under an open source license. The software can be freely downloaded at http://code.google.com/p/peat/downloads/list.
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Affiliation(s)
- Damien Farrell
- School of Biomolecular and Biomedical Science, Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.
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Kessel KA, Habermehl D, Bohn C, Jäger A, Floca RO, Zhang L, Bougatf N, Bendl R, Debus J, Combs SE. [Database supported electronic retrospective analyses in radiation oncology: establishing a workflow using the example of pancreatic cancer]. Strahlenther Onkol 2012; 188:1119-24. [PMID: 23108385 DOI: 10.1007/s00066-012-0214-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Accepted: 07/16/2012] [Indexed: 01/27/2023]
Abstract
PURPOSE Especially in the field of radiation oncology, handling a large variety of voluminous datasets from various information systems in different documentation styles efficiently is crucial for patient care and research. To date, conducting retrospective clinical analyses is rather difficult and time consuming. With the example of patients with pancreatic cancer treated with radio-chemotherapy, we performed a therapy evaluation by using an analysis system connected with a documentation system. MATERIALS AND METHODS A total number of 783 patients have been documented into a professional, database-based documentation system. Information about radiation therapy, diagnostic images and dose distributions have been imported into the web-based system. RESULTS For 36 patients with disease progression after neoadjuvant chemoradiation, we designed and established an analysis workflow. After an automatic registration of the radiation plans with the follow-up images, the recurrence volumes are segmented manually. Based on these volumes the DVH (dose volume histogram) statistic is calculated, followed by the determination of the dose applied to the region of recurrence. All results are saved in the database and included in statistical calculations. CONCLUSION The main goal of using an automatic analysis tool is to reduce time and effort conducting clinical analyses, especially with large patient groups. We showed a first approach and use of some existing tools, however manual interaction is still necessary. Further steps need to be taken to enhance automation. Already, it has become apparent that the benefits of digital data management and analysis lie in the central storage of data and reusability of the results. Therefore, we intend to adapt the analysis system to other types of tumors in radiation oncology.
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Affiliation(s)
- K A Kessel
- Abteilung für Radioonkolgie und Strahlentherapie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Deutschland.
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O’Farrell B, Haase JK, Velayudhan V, Murphy RA, Achtman M. Transforming microbial genotyping: a robotic pipeline for genotyping bacterial strains. PLoS One 2012; 7:e48022. [PMID: 23144721 PMCID: PMC3483277 DOI: 10.1371/journal.pone.0048022] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 09/20/2012] [Indexed: 11/30/2022] Open
Abstract
Microbial genotyping increasingly deals with large numbers of samples, and data are commonly evaluated by unstructured approaches, such as spread-sheets. The efficiency, reliability and throughput of genotyping would benefit from the automation of manual manipulations within the context of sophisticated data storage. We developed a medium- throughput genotyping pipeline for MultiLocus Sequence Typing (MLST) of bacterial pathogens. This pipeline was implemented through a combination of four automated liquid handling systems, a Laboratory Information Management System (LIMS) consisting of a variety of dedicated commercial operating systems and programs, including a Sample Management System, plus numerous Python scripts. All tubes and microwell racks were bar-coded and their locations and status were recorded in the LIMS. We also created a hierarchical set of items that could be used to represent bacterial species, their products and experiments. The LIMS allowed reliable, semi-automated, traceable bacterial genotyping from initial single colony isolation and sub-cultivation through DNA extraction and normalization to PCRs, sequencing and MLST sequence trace evaluation. We also describe robotic sequencing to facilitate cherrypicking of sequence dropouts. This pipeline is user-friendly, with a throughput of 96 strains within 10 working days at a total cost of < €25 per strain. Since developing this pipeline, >200,000 items were processed by two to three people. Our sophisticated automated pipeline can be implemented by a small microbiology group without extensive external support, and provides a general framework for semi-automated bacterial genotyping of large numbers of samples at low cost.
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Affiliation(s)
- Brian O’Farrell
- Environmental Research Institute, University College Cork, Cork, Ireland
- * E-mail: (MA); (JKH); (BOF)
| | - Jana K. Haase
- Environmental Research Institute, University College Cork, Cork, Ireland
- * E-mail: (MA); (JKH); (BOF)
| | | | - Ronan A. Murphy
- Environmental Research Institute, University College Cork, Cork, Ireland
| | - Mark Achtman
- Environmental Research Institute, University College Cork, Cork, Ireland
- * E-mail: (MA); (JKH); (BOF)
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Khansa L, Forcade J, Nambari G, Parasuraman S, Cox P. Proposing an Intelligent Cloud-Based Electronic Health Record System. INTERNATIONAL JOURNAL OF BUSINESS DATA COMMUNICATIONS AND NETWORKING 2012. [DOI: 10.4018/jbdcn.2012070104] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the aging United States population, healthcare costs have considerably increased and are expected to keep rising in the foreseeable future. In this paper, the authors propose an intelligent cloud-based electronic health record (ICEHR) system that has the potential to reduce medical errors and improve patients’ quality of life, in addition to reducing costs and increasing the productivity of healthcare organizations. They developed a set of best practices that encompass end-user policies and regulations, identity and access management, network resilience and service level agreements, advanced computational power, “Big Data” mining abilities, and other operational/managerial controls that are meant to improve the privacy and security of the ICEHR, and make it inherently compliant to healthcare regulations. These best practices serve as a framework that offers a single interconnection agreement between the cloud host and healthcare entities, and streamlines access to private patient information based on a unified set of access principles.
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