Literature DB >> 32926360

Cohort Identification for Translational Bioinformatics Studies.

Tiffany A Lin1,2, Zeynep Eroglu2,3, Rodrigo Carvajal4, Joseph Markowitz5,6.   

Abstract

Translational studies for therapeutic development require cohort identification to identify appropriate biological materials from patients that can be utilized to test a specific hypothesis. Robust health information systems exist, but there are numerous challenges in accessing the information to select appropriate biological specimens needed for translational experiments. This chapter on methods describes the current standard process for cohort identification utilized by the Cutaneous Oncology Program and the Collaborative Data Services Core (CDSC) at Moffitt Cancer Center. The methods include utilization of graphical user interfaces coupled with database querying. As such, this chapter outlines the regulatory and procedural processes needed to utilize a health information management system to filter patients for cohort identification.

Entities:  

Keywords:  Bioinformatics; Cohort identification; Informatics; Resource paper; Translational science

Mesh:

Year:  2021        PMID: 32926360      PMCID: PMC7787345          DOI: 10.1007/978-1-0716-0849-4_3

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  18 in total

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Authors:  M Silver; T Sakata; H C Su; C Herman; S B Dolins; M J O'Shea
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4.  Medical education research and IRB review: an analysis and comparison of the IRB review process at six institutions.

Authors:  Liselotte N Dyrbye; Matthew R Thomas; Alex J Mechaber; Anne Eacker; William Harper; F Stanford Massie; David V Power; Tait D Shanafelt
Journal:  Acad Med       Date:  2007-07       Impact factor: 6.893

5.  The development of health care data warehouses to support data mining.

Authors:  Jason A Lyman; Kenneth Scully; James H Harrison
Journal:  Clin Lab Med       Date:  2008-03       Impact factor: 1.935

6.  Clinical research informatics: challenges, opportunities and definition for an emerging domain.

Authors:  Peter J Embi; Philip R O Payne
Journal:  J Am Med Inform Assoc       Date:  2009-03-04       Impact factor: 4.497

7.  Data Analysis Using R Programming.

Authors:  Bertram K C Chan
Journal:  Adv Exp Med Biol       Date:  2018       Impact factor: 2.622

Review 8.  Big data management challenges in health research-a literature review.

Authors:  Xiaoming Wang; Carolyn Williams; Zhen Hua Liu; Joe Croghan
Journal:  Brief Bioinform       Date:  2019-01-18       Impact factor: 11.622

9.  Using Big Data in oncology to prospectively impact clinical patient care: A proof of concept study.

Authors:  Vérène Dougoud-Chauvin; Jae Jin Lee; Edgardo Santos; Vonetta L Williams; Nicolò M L Battisti; Kavita Ghia; Marina Sehovic; Cortlin Croft; Jongphil Kim; Lodovico Balducci; Julie A Kish; Martine Extermann
Journal:  J Geriatr Oncol       Date:  2018-04-17       Impact factor: 3.599

10.  Physician assessment of disease activity in JIA subtypes. Analysis of data extracted from electronic medical records.

Authors:  Michael L Miller; Jason Ruprecht; Deli Wang; Ying Zhou; George Lales; Sean McKenna; Marisa Klein-Gitelman
Journal:  Pediatr Rheumatol Online J       Date:  2011-04-14       Impact factor: 3.054

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