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Kilicoglu H, Ensan F, McInnes B, Wang LL. Semantics-enabled biomedical literature analytics. J Biomed Inform 2024; 150:104588. [PMID: 38244957 DOI: 10.1016/j.jbi.2024.104588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/22/2024]
Affiliation(s)
- Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana Champaign, Champaign, IL, USA.
| | - Faezeh Ensan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
| | - Bridget McInnes
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
| | - Lucy Lu Wang
- Information School, University of Washington, Seattle, WA, USA.
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Lewis AE, Weiskopf N, Abrams ZB, Foraker R, Lai AM, Payne PRO, Gupta A. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc 2023; 30:1730-1740. [PMID: 37390812 PMCID: PMC10531113 DOI: 10.1093/jamia/ocad120] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/16/2023] [Accepted: 06/23/2023] [Indexed: 07/02/2023] Open
Abstract
OBJECTIVE We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies. MATERIALS AND METHODS We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process. RESULTS We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology. DISCUSSION There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality. CONCLUSION Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.
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Affiliation(s)
- Abigail E Lewis
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Nicole Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Zachary B Abrams
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Randi Foraker
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
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Casotti MC, Meira DD, Alves LNR, Bessa BGDO, Campanharo CV, Vicente CR, Aguiar CC, Duque DDA, Barbosa DG, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Pavan IP, Louro LS, Braga RFR, Trabach RSDR, Louro TS, de Carvalho EF, Louro ID. Translational Bioinformatics Applied to the Study of Complex Diseases. Genes (Basel) 2023; 14:419. [PMID: 36833346 PMCID: PMC9956936 DOI: 10.3390/genes14020419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.
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Affiliation(s)
- Matheus Correia Casotti
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Dummer Meira
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Lyvia Neves Rebello Alves
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Camilly Victória Campanharo
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Creuza Rachel Vicente
- Departamento de Medicina Social, Universidade Federal do Espírito Santo, Vitória 29040-090, Espírito Santo, Brazil
| | - Carla Carvalho Aguiar
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Daniel de Almeida Duque
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Gonçalves Barbosa
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Fernanda Mariano Garcia
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Flávia de Paula
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Gabriel Mendonça Santana
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Isabele Pagani Pavan
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Luana Santos Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Furlani Rocon Braga
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Silva dos Reis Trabach
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Thomas Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Espírito Santo, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcantara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Rio de Janeiro, Brazil
| | - Iúri Drumond Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
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Muto R, Fukuta S, Watanabe T, Shindo Y, Kanemitsu Y, Kajikawa S, Yonezawa T, Inoue T, Ichihashi T, Shiratori Y, Maruyama S. Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data. Front Med (Lausanne) 2022; 9:1042067. [PMID: 36530899 PMCID: PMC9748157 DOI: 10.3389/fmed.2022.1042067] [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: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19. MATERIALS AND METHODS We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission. RESULTS The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care. CONCLUSION In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.
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Affiliation(s)
- Reiko Muto
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Molecular Medicine and Metabolism, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
| | - Shigeki Fukuta
- Artificial Intelligence Laboratory, Fujitsu Limited, Kawasaki, Japan
| | | | - Yuichiro Shindo
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihiro Kanemitsu
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Shigehisa Kajikawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Toshiyuki Yonezawa
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine and Allergology, Aichi Medical University Hospital, Nagakute, Japan
| | - Takahiro Inoue
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuji Ichihashi
- Department of Internal Medicine, Aichi Prefectural Aichi Hospital, Okazaki, Japan
| | - Yoshimune Shiratori
- Center for Healthcare Information Technology (C-HiT), Nagoya University, Nagoya, Japan
- Medical IT Center, Nagoya University Hospital, Nagoya, Japan
| | - Shoichi Maruyama
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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