1
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Morris SM, Gupta A, Kim S, Foraker RE, Gutmann DH, Payne PRO. Predictive Modeling for Clinical Features Associated With Neurofibromatosis Type 1. Neurol Clin Pract 2022; 11:497-505. [PMID: 34987881 PMCID: PMC8723929 DOI: 10.1212/cpj.0000000000001089] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/25/2021] [Indexed: 12/23/2022]
Abstract
Objective To perform a longitudinal analysis of clinical features associated with
neurofibromatosis type 1 (NF1) based on demographic and clinical
characteristics and to apply a machine learning strategy to determine
feasibility of developing exploratory predictive models of optic pathway
glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a
pediatric NF1 cohort. Methods Using NF1 as a model system, we perform retrospective data analyses using a
manually curated NF1 clinical registry and electronic health record (EHR)
information and develop machine learning models. Data for 798 individuals
were available, with 578 comprising the pediatric cohort used for
analysis. Results Males and females were evenly represented in the cohort. White children were
more likely to develop OPG (odds ratio [OR]: 2.11, 95% confidence interval
[CI]: 1.11–4.00, p = 0.02) relative to their
non-White peers. Median age at diagnosis of OPG was 6.5 years
(1.7–17.0), irrespective of sex. Males were more likely than females
to have a diagnosis of ADHD (OR: 1.90, 95% CI: 1.33–2.70,
p < 0.001), and earlier diagnosis in males
relative to females was observed. The gradient boosting classification model
predicted diagnosis of ADHD with an area under the receiver operator
characteristic (AUROC) of 0.74 and predicted diagnosis of OPG with an AUROC
of 0.82. Conclusions Using readily available clinical and EHR data, we successfully recapitulated
several important and clinically relevant patterns in NF1 semiology
specifically based on demographic and clinical characteristics. Naive
machine learning techniques can be potentially used to develop and validate
predictive phenotype complexes applicable to risk stratification and disease
management in NF1.
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Affiliation(s)
- Stephanie M Morris
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Aditi Gupta
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Seunghwan Kim
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Randi E Foraker
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - David H Gutmann
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Philip R O Payne
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
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2
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Acosta-López JE, Suárez I, Pineda DA, Cervantes-Henríquez ML, Martínez-Banfi ML, Lozano-Gutiérrez SG, Ahmad M, Pineda-Alhucema W, Noguera-Machacón LM, Hoz MDL, Mejía-Segura E, Jiménez-Figueroa G, Sánchez-Rojas M, Mastronardi CA, Arcos-Burgos M, Vélez JI, Puentes-Rozo PJ. Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD. Brain Sci 2021; 11:1218. [PMID: 34573239 PMCID: PMC8467181 DOI: 10.3390/brainsci11091218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/19/2022] Open
Abstract
Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients' diagnosis, follow-up, and longitudinal assessment in the clinical setting.
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Affiliation(s)
- Johan E. Acosta-López
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Isabel Suárez
- Universidad del Norte, Barranquilla 081007, Colombia;
| | - David A. Pineda
- Neuropsychology and Conduct Research Group, University of San Buenaventura, Medellín 050010, Colombia;
| | - Martha L. Cervantes-Henríquez
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
- Universidad del Norte, Barranquilla 081007, Colombia;
| | - Martha L. Martínez-Banfi
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Semiramis G. Lozano-Gutiérrez
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Mostapha Ahmad
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Wilmar Pineda-Alhucema
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Luz M. Noguera-Machacón
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Moisés De La Hoz
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Elsy Mejía-Segura
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Giomar Jiménez-Figueroa
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Manuel Sánchez-Rojas
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | | | - Mauricio Arcos-Burgos
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia
| | | | - Pedro J. Puentes-Rozo
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
- Grupo de Neurociencias del Caribe, Universidad del Atlántico, Barranquilla 081007, Colombia
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3
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He W, Kirchoff KG, Sampson RR, McGhee KK, Cates AM, Obeid JS, Lenert LA. Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research. J Am Med Inform Assoc 2021; 28:1440-1450. [PMID: 33729486 DOI: 10.1093/jamia/ocab023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems. MATERIALS AND METHODS In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. RESULTS Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting. DISCUSSION Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. CONCLUSION By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration.
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Affiliation(s)
- Wenjun He
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA
| | - Katie G Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Royce R Sampson
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Kimberly K McGhee
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Academic Affairs Faculty, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew M Cates
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Jihad S Obeid
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Leslie A Lenert
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
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4
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Garza MY, Rutherford M, Myneni S, Fenton S, Walden A, Topaloglu U, Eisenstein E, Kumar KR, Zimmerman KO, Rocca M, Gordon GS, Hume S, Wang Z, Zozus M. Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:472-481. [PMID: 33936420 PMCID: PMC8075534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The direct use of EHR data in research, often referred to as 'eSource', has long-been a goal for researchers because of anticipated increases in data quality and reductions in site burden. eSource solutions should rely on data exchange standards for consistency, quality, and efficiency. The utility of any data standard can be evaluated by its ability to meet specific use case requirements. The Health Level Seven (HL7 ® ) Fast Healthcare Interoperability Resources (FHIR ® ) standard is widely recognized for clinical data exchange; however, a thorough analysis of the standard's data coverage in supporting multi-site clinical studies has not been conducted. We developed and implemented a systematic mapping approach for evaluating HL7 ® FHIR ® standard coverage in multi-center clinical trials. Study data elements from three diverse studies were mapped to HL7 ® FHIR ® resources, offering insight into the coverage and utility of the standard for supporting the data collection needs of multi-site clinical research studies.
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Affiliation(s)
- Maryam Y Garza
- University of Arkansas for Medical Sciences, Little Rock, AR
- University of Texas Health Science Center at Houston, Houston, TX
| | | | - Sahiti Myneni
- University of Texas Health Science Center at Houston, Houston, TX
| | - Susan Fenton
- University of Texas Health Science Center at Houston, Houston, TX
| | - Anita Walden
- Oregon Health and Science University, Portland, OR
| | - Umit Topaloglu
- Wake Forest University School of Medicine, Winston-Salem, NC
| | - Eric Eisenstein
- Duke Clinical Research Institute, Duke University, Durham, NC
| | - Karan R Kumar
- Duke Clinical Research Institute, Duke University, Durham, NC
| | | | - Mitra Rocca
- United States Food & Drug Administration, Silver Springs, MD
| | | | - Sam Hume
- Clinical Data Interchange Standards Consortium, Austin, TX
| | - Zhan Wang
- University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Meredith Zozus
- University of Texas Health Science Center at San Antonio, San Antonio, TX
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5
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Knosp BM, Barnett WK, Anderson NR, Embi PJ. Research IT maturity models for academic health centers: Early development and initial evaluation. J Clin Transl Sci 2018; 2:289-294. [PMID: 30828469 PMCID: PMC6390403 DOI: 10.1017/cts.2018.339] [Citation(s) in RCA: 3] [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: 08/06/2018] [Revised: 10/26/2018] [Accepted: 10/30/2018] [Indexed: 11/07/2022] Open
Abstract
This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.
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Affiliation(s)
- Boyd M. Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA, USA
| | - William K. Barnett
- Regenstrief InstituteInc., Indiana, CTSI, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nicholas R. Anderson
- Clinical Translational Science Center and Department of Public Health Sciences, UC Davis Health System, University of California, Davis, Davis, CA, USA
| | - Peter J. Embi
- Regenstrief InstituteInc., Indiana, CTSI, Indiana University School of Medicine, Indianapolis, IN, USA
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6
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D'Souza M, Sulakhe D, Wang S, Xie B, Hashemifar S, Taylor A, Dubchak I, Conrad Gilliam T, Maltsev N. Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks. Methods Mol Biol 2017; 1613:85-99. [PMID: 28849559 DOI: 10.1007/978-1-4939-7027-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.
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Affiliation(s)
- Mark D'Souza
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA.
- Argonne National Laboratory, Building 221, Room: A142, 9700 South Cass Avenue, Argonne, IL, 60439, USA.
| | - Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
| | - Sheng Wang
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, 60637, USA
| | - Bing Xie
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Somaye Hashemifar
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, 60637, USA
| | - Andrew Taylor
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
| | - Inna Dubchak
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Department of Energy Joint Genome Institute, Walnut Creek, CA, USA
| | - T Conrad Gilliam
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
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7
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Arsoniadis EG, Melton GB. Leveraging the electronic health record for research and quality improvement: Current strengths and future challenges. SEMINARS IN COLON AND RECTAL SURGERY 2016. [DOI: 10.1053/j.scrs.2016.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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8
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Research Infrastructure for Collaborative Team Science: Challenges in Technology-Supported Workflows in and Across Laboratories, Institutions, and Geographies. Semin Nephrol 2016. [PMID: 26215866 DOI: 10.1016/j.semnephrol.2015.04.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Collaborative research has many challenges. One under-researched challenge is how to align collaborators' research practices and evolving analytical reasoning with technologies and configurations of technologies that best support them. The goal of such alignment is to enhance collaborative problem solving capabilities in research. Toward this end, we draw on our own research and a synthesis of the literature to characterize the workflow of collaborating scientists in systems-level renal disease research. We describe the various phases of a hypothetical workflow among diverse collaborators within and across laboratories, extending from their primary analysis through secondary analysis. For each phase, we highlight required technology supports, and. At time, complementary organizational supports. This survey of supports matching collaborators' analysis practices and needs in research projects to technological support is preliminary, aimed ultimately at developing a research capability framework that can help scientists and technologists mutually understand workflows and technologies that can help enable and enhance them.
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REGAN KELLY, ABRAMS ZACHARY, SHARPNACK MICHAEL, SRIVASTAVA ARUNIMA, HUANG KUN, SHAH NIGAM, PAYNE PHILIPR. DISCOVERY OF MOLECULARLY TARGETED THERAPIES. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016; 21:1-8. [PMID: 26776168 PMCID: PMC4874173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Affiliation(s)
- KELLY REGAN
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
| | - ZACHARY ABRAMS
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
| | - MICHAEL SHARPNACK
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
| | - ARUNIMA SRIVASTAVA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
| | - KUN HUANG
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
| | - NIGAM SHAH
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305,
| | - PHILIP R.O. PAYNE
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210,
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10
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Shalaby T, Fiaschetti G, Baumgartner M, Grotzer MA. MicroRNA signatures as biomarkers and therapeutic target for CNS embryonal tumors: the pros and the cons. Int J Mol Sci 2014; 15:21554-86. [PMID: 25421247 PMCID: PMC4264241 DOI: 10.3390/ijms151121554] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 11/07/2014] [Accepted: 11/08/2014] [Indexed: 12/19/2022] Open
Abstract
Embryonal tumors of the central nervous system represent a heterogeneous group of childhood cancers with an unknown pathogenesis; diagnosis, on the basis of histological appearance alone, is controversial and patients’ response to therapy is difficult to predict. They encompass medulloblastoma, atypical teratoid/rhabdoid tumors and a group of primitive neuroectodermal tumors. All are aggressive tumors with the tendency to disseminate throughout the central nervous system. The large amount of genomic and molecular data generated over the last 5–10 years encourages optimism that new molecular targets will soon improve outcomes. Recent neurobiological studies have uncovered the key role of microRNAs (miRNAs) in embryonal tumors biology and their potential use as biomarkers is increasingly being recognized and investigated. However the successful use of microRNAs as reliable biomarkers for the detection and management of pediatric brain tumors represents a substantial challenge. This review debates the importance of miRNAs in the biology of central nervous systemembryonal tumors focusing on medulloblastoma and atypical teratoid/rhabdoid tumors and highlights the advantages as well as the limitations of their prospective application as biomarkers and candidates for molecular therapeutic targets.
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Affiliation(s)
- Tarek Shalaby
- Department of Oncology, University Children's Hospital of Zurich, Steinwiesstrasse 75, Zurich 8032, Switzerland.
| | - Giulio Fiaschetti
- Department of Oncology, University Children's Hospital of Zurich, Steinwiesstrasse 75, Zurich 8032, Switzerland.
| | - Martin Baumgartner
- Department of Oncology, University Children's Hospital of Zurich, Steinwiesstrasse 75, Zurich 8032, Switzerland.
| | - Michael A Grotzer
- Department of Oncology, University Children's Hospital of Zurich, Steinwiesstrasse 75, Zurich 8032, Switzerland.
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Miranda ML, Ferranti J, Strauss B, Neelon B, Califf RM. Geographic health information systems: a platform to support the 'triple aim'. Health Aff (Millwood) 2014; 32:1608-15. [PMID: 24019366 DOI: 10.1377/hlthaff.2012.1199] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Despite the rapid growth of electronic health data, most data systems do not connect individual patient records to data sets from outside the health care delivery system. These isolated data systems cannot support efforts to recognize or address how the physical and environmental context of each patient influences health choices and health outcomes. In this article we describe how a geographic health information system in Durham, North Carolina, links health system and social and environmental data via shared geography to provide a multidimensional understanding of individual and community health status and vulnerabilities. Geographic health information systems can be useful in supporting the Institute for Healthcare Improvement's Triple Aim Initiative to improve the experience of care, improve the health of populations, and reduce per capita costs of health care. A geographic health information system can also provide a comprehensive information base for community health assessment and intervention for accountable care that includes the entire population of a geographic area.
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High-Throughput Translational Medicine: Challenges and Solutions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 799:39-67. [DOI: 10.1007/978-1-4614-8778-4_3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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13
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A flexible approach to distributed data anonymization. J Biomed Inform 2013; 50:62-76. [PMID: 24333850 DOI: 10.1016/j.jbi.2013.12.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Revised: 11/12/2013] [Accepted: 12/04/2013] [Indexed: 11/22/2022]
Abstract
Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets.
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Granger BB, Rusincovitch SA, Avery S, Batch BC, Dunham AA, Feinglos MN, Kelly K, Pierre-Louis M, Spratt SE, Califf RM. Missing signposts on the roadmap to quality: a call to improve medication adherence indicators in data collection for population research. Front Pharmacol 2013; 4:139. [PMID: 24223556 PMCID: PMC3819628 DOI: 10.3389/fphar.2013.00139] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2013] [Accepted: 10/17/2013] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Poor adherence to prescribed medicines is associated with increased rates of poor outcomes, including hospitalization, serious adverse events, and death, and is also associated with increased healthcare costs. However, current approaches to evaluation of medication adherence using real-world electronic health records (EHRs) or claims data may miss critical opportunities for data capture and fall short in modeling and representing the full complexity of the healthcare environment. We sought to explore a framework for understanding and improving data capture for medication adherence in a population-based intervention in four U.S. counties. APPROACH We posited that application of a data model and a process matrix when designing data collection for medication adherence would improve identification of variables and data accessibility, and could support future research on medication-taking behaviors. We then constructed a use case in which data related to medication adherence would be leveraged to support improved healthcare quality, clinical outcomes, and efficiency of healthcare delivery in a population-based intervention for persons with diabetes. Because EHRs in use at participating sites were deemed incapable of supplying the needed data, we applied a taxonomic approach to identify and define variables of interest. We then applied a process matrix methodology, in which we identified key research goals and chose optimal data domains and their respective data elements, to instantiate the resulting data model. CONCLUSIONS Combining a taxonomic approach with a process matrix methodology may afford significant benefits when designing data collection for clinical and population-based research in the arena of medication adherence. Such an approach can effectively depict complex real-world concepts and domains by "mapping" the relationships between disparate contributors to medication adherence and describing their relative contributions to the shared goals of improved healthcare quality, outcomes, and cost.
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Affiliation(s)
- Bradi B Granger
- Department of Nursing, Duke Translational Nursing Institute, Duke University Medical Center Durham, NC, USA
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Kukafka R, Allegrante JP, Khan S, Bigger JT, Johnson SB. Understanding facilitators and barriers to reengineering the clinical research enterprise in community-based practice settings. Contemp Clin Trials 2013; 36:166-74. [DOI: 10.1016/j.cct.2013.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2013] [Revised: 06/12/2013] [Accepted: 06/16/2013] [Indexed: 11/16/2022]
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Payne PRO, Pressler TR, Sarkar IN, Lussier Y. People, organizational, and leadership factors impacting informatics support for clinical and translational research. BMC Med Inform Decis Mak 2013; 13:20. [PMID: 23388243 PMCID: PMC3577661 DOI: 10.1186/1472-6947-13-20] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2012] [Accepted: 01/14/2013] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND In recent years, there have been numerous initiatives undertaken to describe critical information needs related to the collection, management, analysis, and dissemination of data in support of biomedical research (J Investig Med 54:327-333, 2006); (J Am Med Inform Assoc 16:316-327, 2009); (Physiol Genomics 39:131-140, 2009); (J Am Med Inform Assoc 18:354-357, 2011). A common theme spanning such reports has been the importance of understanding and optimizing people, organizational, and leadership factors in order to achieve the promise of efficient and timely research (J Am Med Inform Assoc 15:283-289, 2008). With the emergence of clinical and translational science (CTS) as a national priority in the United States, and the corresponding growth in the scale and scope of CTS research programs, the acuity of such information needs continues to increase (JAMA 289:1278-1287, 2003); (N Engl J Med 353:1621-1623, 2005); (Sci Transl Med 3:90, 2011). At the same time, systematic evaluations of optimal people, organizational, and leadership factors that influence the provision of data, information, and knowledge management technologies and methods are notably lacking. METHODS In response to the preceding gap in knowledge, we have conducted both: 1) a structured survey of domain experts at Academic Health Centers (AHCs); and 2) a subsequent thematic analysis of public-domain documentation provided by those same organizations. The results of these approaches were then used to identify critical factors that may influence access to informatics expertise and resources relevant to the CTS domain. RESULTS A total of 31 domain experts, spanning the Biomedical Informatics (BMI), Computer Science (CS), Information Science (IS), and Information Technology (IT) disciplines participated in a structured surveyprocess. At a high level, respondents identified notable differences in theaccess to BMI, CS, and IT expertise and services depending on the establishment of a formal BMI academic unit and the perceived relationship between BMI, CS, IS, and IT leaders. Subsequent thematic analysis of the aforementioned public domain documents demonstrated a discordance between perceived and reported integration across and between BMI, CS, IS, and IT programs and leaders with relevance to the CTS domain. CONCLUSION Differences in people, organization, and leadership factors do influence the effectiveness of CTS programs, particularly with regard to the ability to access and leverage BMI, CS, IS, and IT expertise and resources. Based on this finding, we believe that the development of a better understanding of how optimal BMI, CS, IS, and IT organizational structures and leadership models are designed and implemented is critical to both the advancement of CTS and ultimately, to improvements in the quality, safety, and effectiveness of healthcare.
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Affiliation(s)
- Philip RO Payne
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Taylor R Pressler
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Indra Neil Sarkar
- Department of Computer Science, Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA
| | - Yves Lussier
- Department of Medicine and Engineering, University of Chicago, Chicago, IL, USA
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Embi PJ, Weir C, Efthimiadis EN, Thielke SM, Hedeen AN, Hammond KW. Computerized provider documentation: findings and implications of a multisite study of clinicians and administrators. J Am Med Inform Assoc 2013; 20:718-26. [PMID: 23355462 PMCID: PMC3721152 DOI: 10.1136/amiajnl-2012-000946] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Objective Clinical documentation is central to the medical record and so to a range of healthcare and business processes. As electronic health record adoption expands, computerized provider documentation (CPD) is increasingly the primary means of capturing clinical documentation. Previous CPD studies have focused on particular stakeholder groups and sites, often limiting their scope and conclusions. To address this, we studied multiple stakeholder groups from multiple sites across the USA. Methods We conducted 14 focus groups at five Department of Veterans Affairs facilities with 129 participants (54 physicians or practitioners, 34 nurses, and 37 administrators). Investigators qualitatively analyzed resultant transcripts, developed categories linked to the data, and identified emergent themes. Results Five major themes related to CPD emerged: communication and coordination; control and limitations in expressivity; information availability and reasoning support; workflow alteration and disruption; and trust and confidence concerns. The results highlight that documentation intertwines tightly with clinical and administrative workflow. Perceptions differed between the three stakeholder groups but remained consistent within groups across facilities. Conclusions CPD has dramatically changed documentation processes, impacting clinical understanding, decision-making, and communication across multiple groups. The need for easy and rapid, yet structured and constrained, documentation often conflicts with the need for highly reliable and retrievable information to support clinical reasoning and workflows. Current CPD systems, while better than paper overall, often do not meet the needs of users, partly because they are based on an outdated ‘paper-chart’ paradigm. These findings should inform those implementing CPD systems now and future plans for more effective CPD systems.
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Affiliation(s)
- Peter J Embi
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
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Kitzmiller JP, Embi PJ, Manickam K, Sweet KM, Phelps MA, Jackson RD, Marsh CB, Sadee W. Program in pharmacogenomics at the Ohio State University Medical Center. Pharmacogenomics 2012; 13:751-6. [PMID: 22594506 DOI: 10.2217/pgs.12.46] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Established in 2002, the Ohio State University Medical Center Program in Pharmacogenomics, lead by Wolfgang Sadee, is comprised of nearly 50 members dedicated to the discovery, investigation and translation of genetic biomarkers with the primary goal of advancing personalized healthcare. This article describes the research teams, bioinformatics infrastructure, supporting laboratories and Centers for Personalized Healthcare and for Clinical and Translational Science, current molecular genetic studies, translational and clinical pharmacogenomic studies, examples of biomarkers under development, and the future directions of the program.
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Payne PRO, Jackson RD, Best TM, Borlawsky TB, Lai AM, James S, Gurcan MN. Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project. J Am Med Inform Assoc 2012; 19:1110-4. [DOI: 10.1136/amiajnl-2011-000736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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Payne PRO, Marsh CB. Towards a "4I" approach to personalized healthcare. Clin Transl Med 2012; 1:14. [PMID: 23369359 PMCID: PMC3560982 DOI: 10.1186/2001-1326-1-14] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 07/25/2012] [Indexed: 11/13/2022] Open
Abstract
Personalized healthcare holds the promise of ensuring that every patient receives optimal wellness promotion and clinical care based upon his or her unique and multi-factorial phenotype, informed by the most up-to-date and contextually relevant science. However, achieving this vision requires the management, analysis, and delivery of complex data, information, and knowledge. While there are well-established frameworks that serve to inform the pursuit of basic science, clinical, and translational research in support of the operationalization of the personalized healthcare paradigm, equivalent constructs that may enable biomedical informatics innovation and practice aligned with such objectives are noticeably sparse. In response to this gap in knowledge, we propose such a framework for the advancement of biomedical informatics in order to address the fundamental information needs of the personalized healthcare domain. This framework, which we refer to as a “4I” approach, emphasizes the pursuit of research and practice that is information-centric, integrative, interactive, and innovative.
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Affiliation(s)
- Philip R O Payne
- The Ohio State University Wexner Medical Center, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH, 43210, USA.
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Pressler TR, Yen PY, Ding J, Liu J, Embi PJ, Payne PRO. Computational challenges and human factors influencing the design and use of clinical research participant eligibility pre-screening tools. BMC Med Inform Decis Mak 2012; 12:47. [PMID: 22646313 PMCID: PMC3407791 DOI: 10.1186/1472-6947-12-47] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2012] [Accepted: 05/30/2012] [Indexed: 11/30/2022] Open
Abstract
Background Clinical trials are the primary mechanism for advancing clinical care and evidenced-based practice, yet challenges with the recruitment of participants for such trials are widely recognized as a major barrier to these types of studies. Data warehouses (DW) store large amounts of heterogenous clinical data that can be used to enhance recruitment practices, but multiple challenges exist when using a data warehouse for such activities, due to the manner of collection, management, integration, analysis, and dissemination of the data. A critical step in leveraging the DW for recruitment purposes is being able to match trial eligibility criteria to discrete and semi-structured data types in the data warehouse, though trial eligibility criteria tend to be written without concern for their computability. We present the multi-modal evaluation of a web-based tool that can be used for pre-screening patients for clinical trial eligibility and assess the ability of this tool to be practically used for clinical research pre-screening and recruitment. Methods The study used a validation study, usability testing, and a heuristic evaluation to evaluate and characterize the operational characteristics of the software as well as human factors affecting its use. Results Clinical trials from the Division of Cardiology and the Department of Family Medicine were used for this multi-modal evaluation, which included a validation study, usability study, and a heuristic evaluation. From the results of the validation study, the software demonstrated a positive predictive value (PPV) of 54.12% and 0.7%, respectively, and a negative predictive value (NPV) of 73.3% and 87.5%, respectively, for two types of clinical trials. Heuristic principles concerning error prevention and documentation were characterized as the major usability issues during the heuristic evaluation. Conclusions This software is intended to provide an initial list of eligible patients to a clinical study coordinators, which provides a starting point for further eligibility screening by the coordinator. Because this software has a high “rule in” ability, meaning that it is able to remove patients who are not eligible for the study, the use of an automated tool built to leverage an existing enterprise DW can be beneficial to determining eligibility and facilitating clinical trial recruitment through pre-screening. While the results of this study are promising, further refinement and study of this and related approaches to automated eligibility screening, including comparison to other approaches and stakeholder perceptions, are needed and future studies are planned to address these needs.
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Affiliation(s)
- Taylor R Pressler
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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22
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Abstract
Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.
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Affiliation(s)
- Michael G Kahn
- Department of Pediatrics, University of Colorado, Aurora, Colorado 80045, USA.
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Abstract
Translational informatics (TI) is extremely important for the pharmaceutical industry, especially as the bar for regulatory approval of new medications is set higher and higher. This paper will explore three specific areas in the drug development lifecycle, from tools developed by precompetitive consortia to standardized clinical data collection to the effective delivery of medications using clinical decision support, in which TI has a major role to play. Advancing TI will require investment in new tools and algorithms, as well as ensuring that translational issues are addressed early in the design process of informatics projects, and also given higher weight in funding or publication decisions. Ultimately, the source of translational tools and differences between academia and industry are secondary, as long as they move towards the shared goal of improving health.
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Payne PR, Embi PJ, Kahn MG. Selected Papers from the 2011 Summit on Clinical Research Informatics. J Biomed Inform 2011; 44 Suppl 1:S54-S55. [DOI: 10.1016/j.jbi.2011.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2011] [Revised: 11/21/2011] [Accepted: 11/21/2011] [Indexed: 12/01/2022]
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Hu H, Correll M, Kvecher L, Osmond M, Clark J, Bekhash A, Schwab G, Gao D, Gao J, Kubatin V, Shriver CD, Hooke JA, Maxwell LG, Kovatich AJ, Sheldon JG, Liebman MN, Mural RJ. DW4TR: A Data Warehouse for Translational Research. J Biomed Inform 2011; 44:1004-19. [PMID: 21872681 DOI: 10.1016/j.jbi.2011.08.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2010] [Revised: 07/05/2011] [Accepted: 08/04/2011] [Indexed: 10/17/2022]
Abstract
The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.
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Affiliation(s)
- Hai Hu
- Windber Research Institute, Windber, PA 15963, USA.
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Borlawsky TB, Lele O, Payne PRO. Research-IQ: development and evaluation of an ontology-anchored integrative query tool. J Biomed Inform 2011; 44 Suppl 1:S56-S62. [PMID: 21821150 DOI: 10.1016/j.jbi.2011.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2011] [Revised: 07/19/2011] [Accepted: 07/22/2011] [Indexed: 10/17/2022]
Abstract
Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput.
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Affiliation(s)
- Tara B Borlawsky
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
| | - Omkar Lele
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
| | - Philip R O Payne
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
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Mirel B, Eichinger F, Keller BJ, Kretzler M. A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology. JOURNAL OF BIOMEDICAL DISCOVERY AND COLLABORATION 2011; 6:1-33. [PMID: 21455901 PMCID: PMC3090070 DOI: 10.5210/disco.v6i0.3410] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2010] [Revised: 03/15/2011] [Accepted: 02/02/2011] [Indexed: 01/23/2023]
Abstract
BACKGROUND Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? RESULTS From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. CONCLUSIONS Our results imply that visualizations should make available to scientific users “bundles of features†consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.
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Buchan NS, Rajpal DK, Webster Y, Alatorre C, Gudivada RC, Zheng C, Sanseau P, Koehler J. The role of translational bioinformatics in drug discovery. Drug Discov Today 2011; 16:426-34. [PMID: 21402166 DOI: 10.1016/j.drudis.2011.03.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Revised: 01/25/2011] [Accepted: 03/07/2011] [Indexed: 12/11/2022]
Abstract
The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical data and patient records has informed decision-making in multiple aspects of drug discovery and development. Here, we review key examples of translational bioinformatics approaches to emphasize its potential to enhance the quality of drug discovery pipelines, reduce attrition rates and, ultimately, lead to more effective treatments.
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Affiliation(s)
- Natalie S Buchan
- GlaxoSmithKline, Computational Biology, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK
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Abstract
Background Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. Results In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium. Conclusions Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.
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Borlawsky TB, Li J, Shagina L, Crowson MG, Liu Y, Friedman C, Lussier YA. Evaluation of an Ontology-anchored Natural Language-based Approach for Asserting Multi-scale Biomolecular Networks for Systems Medicine. SUMMIT ON TRANSLATIONAL BIOINFORMATICS 2010; 2010:6-10. [PMID: 21347135 PMCID: PMC3041541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The ability to adequately and efficiently integrate unstructured, heterogeneous datasets, which are incumbent to systems biology and medicine, is one of the primary limitations to their comprehensive analysis. Natural language processing (NLP) and biomedical ontologies are automated methods for capturing, standardizing and integrating information across diverse sources, including narrative text. We have utilized the BioMedLEE NLP system to extract and encode, using standard ontologies (e.g., Cell Type Ontology, Mammalian Phenotype, Gene Ontology), biomolecular mechanisms and clinical phenotypes from the scientific literature. We subsequently applied semantic processing techniques to the structured BioMedLEE output to determine the relationships between these biomolecular and clinical phenotype concepts. We conducted an evaluation that shows an average precision and recall of BioMedLEE with respect to annotating phrases comprised of cell type, anatomy/disease, and gene/protein concepts were 86% and 78%, respectively. The precision of the asserted phenotype-molecular relationships was 75%.
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Affiliation(s)
- Tara B. Borlawsky
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH
| | - Jianrong Li
- Center for Biomedical Informatics, Dept. of Medicine, The University of Chicago, IL
| | - Lyudmila Shagina
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Matthew G. Crowson
- Center for Biomedical Informatics, Dept. of Medicine, The University of Chicago, IL
| | - Yang Liu
- Center for Biomedical Informatics, Dept. of Medicine, The University of Chicago, IL
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University, New York, NY,Corresponding authors
| | - Yves A. Lussier
- Center for Biomedical Informatics, Dept. of Medicine, The University of Chicago, IL,Corresponding authors
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Sarkar IN. Biomedical informatics and translational medicine. J Transl Med 2010; 8:22. [PMID: 20187952 PMCID: PMC2837642 DOI: 10.1186/1479-5876-8-22] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2009] [Accepted: 02/26/2010] [Indexed: 11/23/2022] Open
Abstract
Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.
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Affiliation(s)
- Indra Neil Sarkar
- Center for Clinical and Translational Science, Department of Microbiology and Molecular Genetics, University of Vermont, College of Medicine, 89 Beaumont Ave, Given Courtyard N309, Burlington, VT 05405, USA.
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