An Efficient Distributed Learning Framework for Integrating Evidence in Clinical Research Networks


Simple item page
dc.contributorPatient-Centered Outcomes Research Institute
dc.contributor.authorChen, Yong
dc.contributor.otherHospital of the University of Pennsylvania
dc.date.accessioned2024-10-25T21:12:00Z
dc.descriptionRare events such as gastrointestinal bleeding, clinical sites independently often contain insufficient number of cases leading to challenges in characterizing the effect sizes of risk factors for adverse events and conducting accurate risk prediction. Individual patient-level information at each site is often protected by privacy regularities and rules, and direct data integration across multiple clinical sites is infeasible or requires a large amount of operational effort. Literature presents gaps in how to fully utilize all electronic health records (EHR) data from different sites to characterize the impacts of risk factors on adverse events following certain medications and how to borrow information using all EHR data across different sites to better predict the incidence of adverse events in patients. In this study, the research team is to develop a framework of distributed algorithms that efficiently synthesize evidence in large clinical data research networks, for studying impacts of risk factors for rare adverse events. The framework of algorithms will be tailored and applied specifically for clinical data research networks (CDRNs), with a focus on enabling more efficient privacy-preserving sharing of data with the PCORI-funded pediatric learning healthy system, PEDSnet.
dc.description.abstractStudy to develop a framework of distributed algorithms that efficiently synthesize evidence in large clinical data research networks for studying impacts of risk factors for rare adverse events.
dc.identifier.urihttps://hdl.handle.net/20.500.14642/837
dc.language.isoen
dc.publisherPEDSnet
dc.rightsa CC-BY 4.0 Attribution license.
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectInvestigator-Led Study
dc.subjectPEDSnet Data Source
dc.subjectPCORI-Funded Research
dc.subject.meshHealth Services Research
dc.subject.meshResearch
dc.subject.meshData Aggregation
dc.subject.meshPathological Conditions, Signs and Symptoms
dc.titleAn Efficient Distributed Learning Framework for Integrating Evidence in Clinical Research Networks
dspace.entity.typeStudy
local.admin.noteStudy PM: Rochelle Jordan, Study Analysts: UNKNOWN https://atlassian.chop.edu/jira/browse/PMO-420
local.contributor.grantProject Program Awards ME-2019C3-18315 and ME-2018C3-14899
local.contributor.siteLeadPEDSnet Data Coordinating Center
local.contributor.siteSponsorPEDSnet Data Coordinating Center
local.identifier.pedsnetid2020.CHEY.PCORI.DCC.2
project.endDatePresent
project.startDate2020
relation.isPublicationOfStudya8a318ab-7703-4f61-9fec-713ac5d4e1e9
relation.isPublicationOfStudy3d570db9-1111-41cc-adec-45c1bd7649b0
relation.isPublicationOfStudye83718d6-f41c-4239-a074-662d4a0301c2
relation.isPublicationOfStudydad6de82-9e1f-44c1-9929-1bbc47f4c8d2
relation.isPublicationOfStudy817fe48f-dfa3-4f6e-be76-1233cdaa8d6b
relation.isPublicationOfStudy17309c91-3297-4d07-a7bb-bc918f1cf89c
relation.isPublicationOfStudy7920caef-ef40-426d-bf26-2b3d0098c3df
relation.isPublicationOfStudy2d0fe043-c806-4053-8759-07b37acb2069
relation.isPublicationOfStudybd1787a7-45f7-40cc-99c6-fa58d053dad9
relation.isPublicationOfStudy.latestForDiscoverya8a318ab-7703-4f61-9fec-713ac5d4e1e9

Files

Domains

PEDSnet Projects