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      A common data model for the standardization of intensive care unit medication features

      research-article
      , PharmD, MSCR, BCCCP, FCCM , , PharmD, MPA, BCCCP, , MD, PhD, , PharmD, BCCCP, MCCM, FCCP, , PharmD, BCPS, BCCCP, , PharmD, BCCCP, , PharmD, FASHP, FCCM, FCCP, BCCCP, , PharmD, , PharmD, BCCCP, , PhD
      JAMIA Open
      Oxford University Press
      artificial intelligence, common data model, critical care

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          Abstract

          Objective

          Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts.

          Materials and Methods

          A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles.

          Results

          Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online.

          Conclusion

          The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

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          Most cited references54

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

            Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.
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              The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model.

              To develop an acute kidney injury risk prediction model using electronic health record data for longitudinal use in hospitalized patients.
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                Author and article information

                Contributors
                Journal
                JAMIA Open
                JAMIA Open
                jamiaoa
                JAMIA Open
                Oxford University Press
                2574-2531
                July 2024
                02 May 2024
                02 May 2024
                : 7
                : 2
                : ooae033
                Affiliations
                Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy , Augusta, GA 30912, United States
                Department of Pharmacy, Augusta University Medical Center , Augusta, GA 30912, United States
                Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University , Atlanta, GA 30322, United States
                Northeastern University School of Pharmacy , Boston, MA 02115, United States
                Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital , Boston, MA 02115, United States
                Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy , Athens, GA 30601, United States
                Department of Pharmacy, University of North Carolina Medical Center , Chapel Hill, NC 27514, United States
                Department of Pharmacy, Banner University Medical Center Phoenix , Phoenix, AZ 85032, United States
                Department of Pharmacy, Oregon Health and Science University , Portland, OR 97239, United States
                Department of Biomedical Informatics, Emory University School of Medicine , Atlanta, GA 30322, United States
                Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, GA 30322, United States
                Department of Biomedical Informatics, Emory University School of Medicine , Atlanta, GA 30322, United States
                Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, GA 30322, United States
                Author notes
                Corresponding author: Andrea Sikora, PharmD, MSCR, BCCCP, FCCM, Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, 1120 15th Street, HM-118, Augusta, GA 30912, United States ( sikora@ 123456uga.edu )

                Additional information: The investigators in the MRC-ICU Investigator Team are available in the Acknowledgments section.

                Author information
                https://orcid.org/0000-0003-2020-0571
                https://orcid.org/0000-0002-3385-8105
                Article
                ooae033
                10.1093/jamiaopen/ooae033
                11064096
                38699649
                2b0a9303-c661-44c9-8103-811a2ed3b317
                © The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 February 2023
                : 12 February 2024
                : 04 April 2024
                : 09 April 2024
                Page count
                Pages: 9
                Funding
                Funded by: Agency of Healthcare Research and Quality;
                Award ID: R21HS028485
                Award ID: R01HS029009
                Categories
                Research and Applications
                AcademicSubjects/SCI01530
                AcademicSubjects/MED00010
                AcademicSubjects/SCI01060

                artificial intelligence,common data model,critical care

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