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Invited Commentary: Go BIG and Go Global-Executing Large-Scale, Multisite Pharmacoepidemiologic Studies Using Real-World data

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2022年第8期191卷 1368-1371页

作者： Maro, Judith C. Toh, Sengwee Harvard Pilgrim Hlth Care Inst Dept Populat Med 401 Pk DrSuite 401 East Boston MA 02215 USA Harvard Med Sch 401 Pk DrSuite 401 East Boston MA 02215 USA

At the time medical products are approved, we rarely know enough about their comparative safety and effectiveness vis-a-vis alternative therapies to advise patients and providers. Postmarket generation of evidence on rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records that can provide complete capture of data on patient experiences. In the accompanying article by Pradhan et al. (Am J Epidemiology. 2022;XXX(XX):XXX-XXX), the authors demonstrate how observational database studies are often the most practical approach, provided these databases are carefully chosen to be "fit for purpose." Distributed data networks with common data models have proliferated in the last 2 decades in pharmacoepidemiology, allowing efficient capture of patient data in a standardized and structured format across disparate real-world data sources. Use of common data models facilitates transparency by allowing standardized programming approaches that can be easily reproduced. The distributed data network architecture, combined with a common data approach, supports not only multisite observational studies but also pragmatic clinical trials. It also helps bridge international boundaries and further increases the sample size and diversity of study populations.

关键词： common data model multisite studies networks pharmacoepidemiology real-world data

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Goodbye Electronic Health Record?

Goodbye Electronic Health Record?

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European-Federation-for-Medical-Informatics (EFMI) Special Topic Conference (STC) on Digital Professionalism in Health and Care - Developing the Workforce, Building the Future

作者： Hammond, W. Ed Bent, Brinnae West, Vivian L. Duke Med Ctr Duke Ctr Hlth Informat CTSI Durham NC 27710 USA Edge Analyt Inc Campbell CA USA

ISBN: (纸本)9781643683119;9781643683102

The Electronic Health Record has failed to meet its intended purpose. We propose a new approach focusing on the use of data for health and health care. The first step is to create a repository of all patient data with data storage independent of data use. All use functionality is external to data storage. We propose the development of a common data model in which data elements have a rich set of attributes including actionable knowledge. Finally, functionality is provided through a series of application program interfaces (API). New APIs will address directly new methods for using data to increase the effectiveness of data application to improve management of the health and care of a patient. Together these components will open a pathway to finally accomplish the goals of a better future health system.

关键词： EHR patient data repository common data model APIs

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Formalization and Processing of data Requirements for the Development of Next Generation Railway Traffic Management Systems 4th

Formalization and Processing of Data Requirements for the De...

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4th International Conference on Reliability, Safety, and Security of Railway Systems (RSSRail)

作者： Magnien, Airy Cecchetti, Gabriele Ruscelli, Anna Lina Hyde, Paul Liu, Jin Wegele, Stefan SNCF Paris France UIC Paris France Scuola Super Sant Anna Pisa Italy Newcastle Univ Sch Engn Newcastle Upon Tyne Tyne & Wear England Siemens Munich Germany

ISBN: (纸本)9783031058134;9783031058141

Railway Traffic Management Systems (TMSs) handle data from multiple railway subsystems, including Rail Business Services (such as interlocking, RBC, maintenance service, etc.) and external services (such as passenger information systems, weather forecast, etc.). In turn, the data from these subsystems are described in several models or ontologies contributed by various organizations or projects which are in a process of converging or federation. The challenge of the Shift2Rail OPTIMA project, which is implementing a communication platform for virtual testing of new applications for railway TMS, is to allow the exchange of data between different services or users and to support new traffic management applications, enabling access to a large number of disparate data sources. In this paper, the core activities of the OPTIMA project related to the formulation and standardization of a common data model are described. A new common data model is developed based on standardized data structures to enable the seamless exchange of large amounts of data between different and heterogeneous sources and consumers of data, that contributes to the building of next generation of a more effective and efficient railway TMS suitable to offer precise and real-time traffic information to railway operators and other end users.

关键词： Railway Traffic Management System common data model model transformation Context-free grammar

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Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第11期28卷 2313-2324页

作者： Na, Jie Zong, Nansu Wang, Chen Midthun, David E. Luo, Yuan Yang, Ping Jiang, Guoqian Mayo Clin Dept Quantitat Hlth Sci Rochester MN 55905 USA Mayo Clin Dept Artificial Intelligence & Informat 200 First St SW Rochester MN 55905 USA Mayo Clin Dept Internal Med Rochester MN 55905 USA Northwestern Univ Dept Prevent Med Chicago IL USA

Objective: The study sought to test the feasibility of conducting a phenome-wide association study to characterize phenotypic abnormalities associated with individuals at high risk for lung cancer using electronic health records. Materials and Methods: We used the beta release of the All of Us Researcher Workbench with clinical and survey data from a population of 225 000 subjects. We identified 3 cohorts of individuals at high risk to develop lung cancer based on (1) the 2013 U.S. Preventive Services Task Force criteria, (2) the long-term quitters of cigarette smoking criteria, and (3) the younger age of onset criteria. We applied the logistic regression analysis to identify the significant associations between individuals' phenotypes and their risk categories. We validated our findings against a lung cancer cohort from the same population and conducted an expert review to understand whether these associations are known or potentially novel. Results: We found a total of 214 statistically significant associations (P< .05 with a Bonferroni correction and odds ratio > 1.5) enriched in the high-risk individuals from 3 cohorts, and 15 enriched in the low-risk individuals. Forty significant associations enriched in the high-risk individuals and 13 enriched in the low-risk individuals were validated in the cancer cohort. Expert review identified 15 potentially new associations enriched in the high-risk individuals. Conclusions: It is feasible to conduct a phenome-wide association study to characterize phenotypic abnormalities associated in high-risk individuals developing lung cancer using electronic health records. The All of Us Research Workbench is a promising resource for the research studies to evaluate and optimize lung cancer screening criteria.

关键词： Phenome Wide Association Study (PheWAS) Electronic Health Records (EHRs) lung cancer screening all of us research program common data model

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Increase transparency and reproducibility of real-world evidence in rare diseases through disease-specific Federated data Networks

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2024年第4期33卷 e5778-e5778页

作者： van Baalen, Valerie Didden, Eva-Maria Rosenberg, Daniel Bardenheuer, Kristina van Speybroeck, Michel Brand, Monika Johnson & Johnson Off Chief Med Officer Global Epidemiol Basel Switzerland Johnson & Johnson Hlth Econ Market Access & Reimbursement EMEA Real World Evidence & Value Based Hlth Care Neuss Germany Johnson & Johnson Technol Data Sci IT EMEA Beerse Belgium

Purpose In rare diseases, real-world evidence (RWE) generation is often restricted due to small patient numbers and global geographic distribution. A federated data network (FDN) approach brings together multiple data sources harmonized for collaboration to increase the power of observational research. In this paper, we review how to increase reproducibility and transparency of RWE studies in rare diseases through disease-specific FDNs. Method To be successful, a multiple stakeholder scientific FDN collaboration requires a strong governance model in place. In such a model, each database owner remains in full control regarding the use of and access to patient-level data and is responsible for data privacy, ethical, and legal compliance. Provided that all this is well documented and good database descriptions are in place, such a governance model results in increased transparency, while reproducibility is achieved through data curation and harmonization, and distributed analytical methods. Results Leveraging the OHDSI community set of methods and tools, two rare disease-specific FDNs are discussed in more detail. For multiple myeloma, HONEUR-the Haematology Outcomes Network in Europe-has built a strong community among the data partners dedicated to scientific exchange and research. To advance scientific knowledge in pulmonary hypertension (PH) an FDN, called PHederation, was established to form a partnership of research institutions with PH databases coming from diverse origins.

关键词： common data model federated data network governance rare disease real-world evidence reproducibility transparency

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Toward new generation railway Traffic Management Systems: the contribution of the OPTIMA project

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Transportation Research Procedia 2023年 72卷 3166-3173页

作者： Gabriele Cecchetti Anna Lina Ruscelli Cristian Ulianov Paul Hyde Jin Liu Airy Magnien Marek Tavač Michal Ďuračík Luca Oneto Jose Bertolin Scuola Superiore Sant'Anna Via G. Moruzzi 1 56124 Pisa Italy University of Newcasle Newcastle upon Tyne NE1 7RU UK University of Zˇilina Univerzitna 8215/1 010 26 Zilina Slovakia University of Genoa Via Opera Pia 11a 16145 Genova Italy UNIFE Av. Louise 221 1050 Bruxelles Belgium

Legacy Traffic Management Systems (TMS) lack interoperable data structures and standardized communication interfaces. The H2020 Shift2Rail OPTIMA project is developing a demonstrator of a communication platform for a new generation of TMS with advanced functionalities for improving the efficiency, effectiveness, and resilience of railway operations, as well as improved interoperability, modularity, and scalability. In this paper, this platform is described, along with its main components that provide seamless access to persistent and real-time data from heterogeneous data sources. A common data model supports the automated data exchange process by means of a fully standardized structure for representing the data. Furthermore, the testing and validation of the OPTIMA platform and of connected TMS services when it is set up as a testing environment are illustrated.

关键词： Railway Traffic Management Systems Integration Layer common data model Technological Demonstrator Integration of networks and services

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Increasing trust in real-world evidence through evaluation of observational data quality

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第10期28卷 2251-2257页

作者： Blacketer, Clair Defalco, Frank J. Ryan, Patrick B. Rijnbeek, Peter R. Janssen Res & Dev LLC Observat Hlth Data Analyt 1125 Trenton Harbourton Rd Titusville NJ 08560 USA Erasmus MC Dept Med Informat Rotterdam Netherlands Columbia Univ Dept Biomed Informat New York NY USA

Objective: Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. Materials and Methods: We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall realworld evidence generation and dissemination process to promote transparency and build confidence in the resulting output. Results: The data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks. Discussion: Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research. Conclusion: Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

关键词： data Quality data Standardization common data model Real World Evidence

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Deep-learning-based automated terminology mapping in OMOP-CDM

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第7期28卷 1489-1496页

作者： Kang, Byungkon Yoon, Jisang Kim, Ha Young Jo, Sung Jin Lee, Yourim Kam, Hye Jin State Univ New York Dept Comp Sci Incheon South Korea Yonsei Univ Grad Sch Informat Seoul South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang North Gyeongsan South Korea EvidNet RWE Analyt Seongnam Si Gyeonggi Do South Korea Hanwha Life New Business Unit Healthcare Life Solut Cluster 5063 Ro Seoul South Korea

Objective: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping. Materials and Methods: To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings. Results: The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements. Discussion: The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system. Conclusion: Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.

关键词： deep-learning terminology mapping embedding common data model automated mapping

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ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2023年第1期177卷 105144-105144页

作者： Oniani, David Parmanto, Bambang Saptono, Andi Bove, Allyn Freburger, Janet Visweswaran, Shyam Cappella, Nickie McLay, Brian Silverstein, Jonathan C. Becich, Michael J. Delitto, Anthony Skidmore, Elizabeth Wang, Yanshan Univ Pittsburgh Dept Hlth Informat Management Pittsburgh PA 15213 USA Univ Pittsburgh Dept Occupat Therapy Pittsburgh PA USA Univ Pittsburgh Dept Phys Therapy Pittsburgh PA USA Univ Pittsburgh Sch Med Dept Biomed Informat Pittsburgh PA 15206 USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Clin & Translat Sci Inst Pittsburgh PA 15213 USA

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

关键词： Electronic health records Rehabilitation Clinical research informatics Informatics infrastructure Natural language processing common data model

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A Predictive model Development Framework Based on Machine-Learning and Stacking Ensemble Technique for Patient Prognosis 24

A Predictive Model Development Framework Based on Machine-Le...

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Proceedings of the 11th International Conference on Bioinformatics Research and Applications

作者： Ji Hyun Lee Hyun Woo Lee Dong Hyun Kim Borim Ryu Department of Radiology Seoul Metropolitan Government?Seoul National University Boramae Medical Center Seoul South Korea Division of Pulmonary and Critical Care Medicine Department of Internal Medicine Seoul Metropolitan Government?Seoul National University Boramae Medical Center Seoul South Korea Center for Data Science Biomedical Research Institute Seoul Metropolitan Government?Seoul National University Boramae Medical Center Seoul South Korea

ISBN: (纸本)9798400717536

This research designed a machine-learning framework to predict patient death in hospitals, utilizing real-world clinical data from patients diagnosed with pneumonia. Predicting in-hospital mortality among high-risk patients is a significant challenge in healthcare domain. Specifically, estimating the risk of complications in many pneumonia patients hinges on the analysis of pathologic and radiologic data, a process that is both expensive and labor-intensive. In this work, we introduce a machine learning approach designed to predict mortality among high-risk pneumonia patients using real clinical historical data, including diagnosis codes, prescription codes, and lab test codes, from individuals hospitalized with pneumonia. Specifically, the model was developed using the data converted into a 'common data model' format, standardizing hospital records into a unified structure. We demonstrate two types of predictive models: 1) traditional machine learning algorithm-based models and 2) hybrid stacking-based ensemble models. The mortality prediction model for approximately 2,540 patients diagnosed with pneumonia was evaluated in terms of precision, recall, F1-measure, and area under the curve (AUC). The experimental results show that our methods outperform standard classification models and, particularly, identify which factors based on the features input into the model influence the risk of death in patients. Since this model is based on the common data model, it has the advantage of being extendable to other hospitals that hold this type of data in the future.

关键词： Observation Medical Outcomes Partnership common data model Pneumonia machine learning

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