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Mateus P, Moonen J, Beran M, Jaarsma E, van der Landen SM, Heuvelink J, Birhanu M, Harms AGJ, Bron E, Wolters FJ, Cats D, Mei H, Oomens J, Jansen W, Schram MT, Dekker A, Bermejo I. Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study. J Biomed Inform 2024; 155:104661. [PMID: 38806105 DOI: 10.1016/j.jbi.2024.104661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. METHODS In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. RESULTS We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. CONCLUSION In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.
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Affiliation(s)
- Pedro Mateus
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands.
| | - Justine Moonen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Magdalena Beran
- Department of Internal Medicine, School for Cardiovascular Diseases (CARIM), Maastricht University, Maastricht, Netherlands; Department of Epidemiology and Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eva Jaarsma
- Center for Nutrition, Prevention, and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands; Amsterdam UMC location Vrije Universiteit Amsterdam, Epidemiology and Data Science, Amsterdam, Netherlands
| | - Sophie M van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Joost Heuvelink
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
| | - Mahlet Birhanu
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Alexander G J Harms
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Esther Bron
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Frank J Wolters
- Erasmus MC - University Medical Centre Rotterdam, Departments of Epidemiology and Radiology & Nuclear Medicine, Netherlands
| | - Davy Cats
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Netherlands
| | - Julie Oomens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Netherlands
| | - Willemijn Jansen
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Netherlands
| | - Miranda T Schram
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands; MHeNS School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; Heart and Vascular Center, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
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Montomoli J, Bitondo MM, Cascella M, Rezoagli E, Romeo L, Bellini V, Semeraro F, Gamberini E, Frontoni E, Agnoletti V, Altini M, Benanti P, Bignami EG. Algor-ethics: charting the ethical path for AI in critical care. J Clin Monit Comput 2024:10.1007/s10877-024-01157-y. [PMID: 38573370 DOI: 10.1007/s10877-024-01157-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.
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Affiliation(s)
- Jonathan Montomoli
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy.
- Health Services Research, Evaluation and Policy Unit, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy.
| | - Maria Maddalena Bitondo
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy
| | - Marco Cascella
- Unit of Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana, " University of Salerno, Baronissi, Salerno, Italy
| | - Emanuele Rezoagli
- School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore, 48, Monza, 20900, Italy
- Dipartimento di Emergenza e Urgenza, Terapia intensiva e Semintensiva adulti e pediatrica, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi, 33, Monza, 20900, Italy
| | - Luca Romeo
- Department of Economics and Law, University of Macerata, Macerata, 62100, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43125, Italy
| | - Federico Semeraro
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Ospedale Maggiore Carlo Alberto Pizzardi, Largo Bartolo Nigrisoli, 2, Bologna, 40133, Italy
| | - Emiliano Gamberini
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy
| | - Emanuele Frontoni
- Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, 62100, Italy
| | - Vanni Agnoletti
- Department of Surgery and Trauma, Anesthesia and Intensive Care Unit, Maurizio Bufalini Hospital, Romagna Local Health Authority, Viale Giovanni Ghirotti, 286, Cesena, 47521, Italy
| | - Mattia Altini
- Hospital Care Sector, Emilia-Romagna Region, Via Aldo Moro, 21, Bologna, 40127, Italy
| | - Paolo Benanti
- Pontifical Gregorian University, Piazza della Pilotta 4, Roma, 00187, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43125, Italy
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Peng Y, Bathelt F, Gebler R, Gött R, Heidenreich A, Henke E, Kadioglu D, Lorenz S, Vengadeswaran A, Sedlmayr M. Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review. JMIR Med Inform 2024; 12:e52967. [PMID: 38354027 PMCID: PMC10902772 DOI: 10.2196/52967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. OBJECTIVE In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. METHODS We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. RESULTS Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. CONCLUSIONS Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.
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Affiliation(s)
- Yuan Peng
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | | | - Richard Gebler
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Robert Gött
- Core Unit Datenintegrationszentrum, University Medicine Greifswald, Greifswald, Germany
| | - Andreas Heidenreich
- Department for Information and Communication Technology (DICT), Data Integration Center (DIC), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Elisa Henke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dennis Kadioglu
- Department for Information and Communication Technology (DICT), Data Integration Center (DIC), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Institute for Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Stephan Lorenz
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Abishaa Vengadeswaran
- Institute for Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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Sundby RT, Rhodes SD, Komlodi-Pasztor E, Sarnoff H, Grasso V, Upadhyaya M, Kim A, Evans DG, Blakeley JO, Hanemann CO, Bettegowda C. Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials. Clin Trials 2024; 21:40-50. [PMID: 37904489 PMCID: PMC10922556 DOI: 10.1177/17407745231203330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
INTRODUCTION Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies. METHODS The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings. RESULTS Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration. CONCLUSION These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.
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Affiliation(s)
- R Taylor Sundby
- Pediatric Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Steven D Rhodes
- Division of Hematology/Oncology/Stem Cell Transplant, Department of Pediatrics, Herman B Wells Center for Pediatric Research, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Edina Komlodi-Pasztor
- Department of Neurology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Herb Sarnoff
- Research and Development, Infixion Bioscience, Inc., San Diego, CA, USA
- Patient Representative, REiNS International Collaboration, San Diego, CA, USA
| | - Vito Grasso
- Neural Stem Cell Institute, Rensselaer, NY, USA
- Patient Representative, REiNS International Collaboration, Troy, NY, USA
| | - Meena Upadhyaya
- Division of Cancer and Genetics, Cardiff University, Wales, UK
| | - AeRang Kim
- Center for Cancer and Blood Disorders, Children’s National Hospital, Washington, DC, USA
| | - D Gareth Evans
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester Academic Health Sciences Centre (MAHSC), ERN GENTURIS, Division of Evolution, Infection and Genomics, The University of Manchester, Manchester, UK
| | - Jaishri O Blakeley
- Division of Neuro-Oncology, Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | | | - Chetan Bettegowda
- Department of Neurosurgery, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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Carlson B, Watkins M, Li M, Furner B, Cohen E, Volchenboum SL. Using A Standardized Nomenclature to Semantically Map Oncology-Related Concepts from Common Data Models to a Pediatric Cancer Data Model. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:874-883. [PMID: 38222364 PMCID: PMC10785885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
The Pediatric Cancer Data Commons (PCDC) comprises an international community whose ironclad commitment to data sharing is combatting pediatric cancer in an unprecedented way. The byproduct of their data sharing efforts is a gold-standard consensus data model covering many types of pediatric cancer. This article describes an effort to utilize SSSOM, an emerging specification for semantically-rich data mappings, to provide a "hub and spoke" model of mappings from several common data models (CDMs) to the PCDC data model. This provides important contributions to the research community, including: 1) a clear view of the current coverage of these CDMs in the domain of pediatric oncology, and 2) a demonstration of creating standardized mappings. These mappings can allow downstream crosswalk for data transformation and enhance data sharing. This can guide those who currently create and maintain brittle ad hoc data mappings in order to utilize the growing volume of viable research data.
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Affiliation(s)
- Bradley Carlson
- Department of Pediatrics, University of Chicago, Chicago, IL
| | - Michael Watkins
- Department of Pediatrics, University of Chicago, Chicago, IL
| | - Mei Li
- Department of Pediatrics, University of Chicago, Chicago, IL
| | - Brian Furner
- Department of Pediatrics, University of Chicago, Chicago, IL
| | - Ellen Cohen
- Department of Pediatrics, University of Chicago, Chicago, IL
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Coiera E. The standard problem. J Am Med Inform Assoc 2023; 30:2086-2097. [PMID: 37654094 PMCID: PMC10654885 DOI: 10.1093/jamia/ocad176] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 09/02/2023] Open
Abstract
OBJECTIVE This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed. METHODS Beginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system. RESULTS The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents. DISCUSSION Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time. CONCLUSION This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.
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Affiliation(s)
- Enrico Coiera
- Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
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Ramakrishnaiah Y, Macesic N, Webb GI, Peleg AY, Tyagi S. EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. J Biomed Inform 2023; 147:104509. [PMID: 37827477 DOI: 10.1016/j.jbi.2023.104509] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
The adoption of electronic health records (EHRs) has created opportunities to analyse historical data for predicting clinical outcomes and improving patient care. However, non-standardised data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardisation module and the preprocessing module. The data standardisation module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.
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Affiliation(s)
- Yashpal Ramakrishnaiah
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia
| | - Nenad Macesic
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia
| | - Geoffrey I Webb
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia
| | - Anton Y Peleg
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia.
| | - Sonika Tyagi
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; School of Computing Technologies, RMIT University, Melbourne 3000, VIC, Australia.
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Sanchez-Pinto LN, Bhavani SV, Atreya MR, Sinha P. Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care. Crit Care Clin 2023; 39:627-646. [PMID: 37704331 DOI: 10.1016/j.ccc.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.
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Affiliation(s)
- Lazaro N Sanchez-Pinto
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
| | | | - Mihir R Atreya
- Division of Critical Care Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - Pratik Sinha
- Division of Clinical and Translational Research, Department of Anesthesia, Washington University School of Medicine, 1 Barnes Jewish Hospital Plaza, St. Louis, MO 63110, USA; Division of Critical Care, Department of Anesthesia, Washington University School of Medicine, 1 Barnes Jewish Hospital Plaza, St. Louis, MO 63110, USA
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Dhingra LS, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. Am J Cardiol 2023; 203:136-148. [PMID: 37499593 PMCID: PMC10865722 DOI: 10.1016/j.amjcard.2023.06.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
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Affiliation(s)
| | - Miles Shen
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Internal Medicine
| | - Anjali Mangla
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
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Choi IY. Present and Future of Utilizing Healthcare Data. Healthc Inform Res 2023; 29:1-3. [PMID: 36792095 PMCID: PMC9932309 DOI: 10.4258/hir.2023.29.1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Affiliation(s)
- In Young Choi
- Chairman of the Board of the Korean Society of Medical Informatics, The Catholic University of Korea College of Medicine, Seoul, Korea
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11
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Chung TK, Jeon Y, Hong Y, Hong S, Moon JS, Lee H. Factors affecting the changes in antihypertensive medications in patients with hypertension. Front Cardiovasc Med 2022; 9:999548. [PMID: 36247446 PMCID: PMC9561640 DOI: 10.3389/fcvm.2022.999548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
As frequent changes in anti-hypertensive (HTN) medications may reduce adherence to the treatments, identifying modifiable factors leading to changes in anti-HTN medications can help clinicians optimize treatment strategies for individual patients. We performed this study to explore the pattern of anti-HTN medications and to identify factors that are associated with the changes in anti-HTN medications. To this end, we used a clinical database of Seoul National University Hospital, extracted, transformed, and loaded by the observational medical outcomes partnership common data model. Demographic and all recorded clinical diagnoses, medications, and procedures data of eligible subjects were collected. Of 636 subjects who were eligible for this study, 297 subjects with a record of ≥1 anti-HTN medication changes and other 297 subjects without a record of medication change were selected for the study population. High diastolic blood pressure (adjusted odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.001–1.040, p = 0.040), arrhythmia (adjusted OR: 10.01, 95% CI: 1.86–185.57, p = 0.030), and angina pectoris with antianginal agents (adjusted OR: 4.85, CI: 1.05–23.89, p = 0.046) were associated with the changes in anti-HTN medications, indicating that any patients with these covariates require additional attention to reduce the likelihood of changing anti-HTN medications.
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Affiliation(s)
- Tae Kyu Chung
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
| | - Yoomin Jeon
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
| | - YeSol Hong
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Suyeon Hong
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Jun Sik Moon
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Howard Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
- Center for Convergence Approaches in Drug Development, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, South Korea
- Advanced Institutes of Convergence Technology, Suwon, South Korea
- *Correspondence: Howard Lee
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