Distributed learning from multiple EHR databases: Contextual embedding models for medical events

Li, Ziyi; Roberts, Kirk; Jiang, Xiaoqian; Long, Qi

doi:10.1016/j.jbi.2019.103138

Cited by 35 publications

(17 citation statements)

References 28 publications

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“…Technically, one can assume that similar results might be expected for other medical deep learning use cases, since generally FL should be able to approach CDS by increasing the rate of synchronization at the cost of network communication overhead. However, we acknowledge that the synchronization used in this study (1 epoch per synchronization, i.e., federated round) may be insufficient for data such as electronic health records 28,29 and clinical notes, as well as genomics, where more variance might be present across international institutions. Notably, we did not perform hyper-parameter tuning specifically to FL.…”

Section: Discussionmentioning

confidence: 99%

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Sheller

Edwards

Reina

et al. 2020

Sci Rep

741

371

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Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine. Abbreviations CDS Collaborative data sharing FL Federated learning IIL Institutional incremental learning CIIL Cyclic institutional incremental learning IID Independent and identically distributed BraTS Brain tumor segmentation

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Section: Discussionmentioning

confidence: 99%

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Sheller

Edwards

Reina

et al. 2020

Sci Rep

741

371

View full text Add to dashboard Cite

show abstract

“…Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy [9,25,34,45,65,82]. In these scenarios, the performance of ML model will be significantly improved by the iterative improvements of learning from large and diverse medical data sets.…”

Section: Healthcarementioning

confidence: 99%

Federated Learning for Healthcare Informatics

Glicksberg

et al. 2020

J Healthc Inform Res

858

367

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“…The SMART COVID Navigator allows the user to log into 2 EHRs: the Veterans Affairs (VA) and the Center for Medicare and Medicaid Services (CMS). There are significant advantages to the application being able to log into multiple EHR servers [ 12 ]. Doctors viewing their patient’s medical information through the application have the ability to discern any discrepancies between the data sources.…”

Section: Methodsmentioning

confidence: 99%

SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study

Suraj¹,

Fitz²,

Kleiman³

et al. 2022

J Med Internet Res

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Background COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19’s effect on patients with certain pre-existing conditions. Objective In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient’s electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. Methods The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient’s medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient’s medical history will affect their COVID-19 outcomes. Results The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources–enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. Conclusions The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient’s medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.

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Distributed learning from multiple EHR databases: Contextual embedding models for medical events

Cited by 35 publications

References 28 publications

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Federated Learning for Healthcare Informatics

SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study

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