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10.1093/jamia/ocaa196 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa196 32805036!7454687!32805036     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (3): 427-443 Nephropedia Template TP {{tp|p=32805036|t=2021. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment |pdf=|usr=}}{{32805036}} gab.com Text The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32805036 #FOAvip OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. Twit Text FOAVip The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32805036 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32805036 #FOAvip English Wikipedia <ref>{{Cite pmid|32805036}}</ref> The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment #MMPMID32805036 Haendel MA; Chute CG; Bennett TD; Eichmann DA; Guinney J; Kibbe WA; Payne PRO; Pfaff ER; Robinson PN; Saltz JH; Spratt H; Suver C; Wilbanks J; Wilcox AB; Williams AE; Wu C; Blacketer C; Bradford RL; Cimino JJ; Clark M; Colmenares EW; Francis PA; Gabriel D; Graves A; Hemadri R; Hong SS; Hripscak G; Jiao D; Klann JG; Kostka K; Lee AM; Lehmann HP; Lingrey L; Miller RT; Morris M; Murphy SN; Natarajan K; Palchuk MB; Sheikh U; Solbrig H; Visweswaran S; Walden A; Walters KM; Weber GM; Zhang XT; Zhu RL; Amor B; Girvin AT; Manna A; Qureshi N; Kurilla MG; Michael SG; Portilla LM; Rutter JL; Austin CP; Gersing KR J Am Med Inform Assoc 2021[Mar]; 28 (3): 427-443 PMID32805036show ga OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. |*COVID-19[MESH] |*Information Dissemination[MESH] |*Intersectoral Collaboration[MESH] |Computer Security[MESH] |Data Analysis[MESH] |Data Science/*organization & administration[MESH] |Ethics Committees, Research[MESH] |Government Regulation[MESH] |Humans[MESH] |National Institutes of Health (U.S.)[MESH]The National COVID Cohort Collaborative (N3C): Rationale, design, infr(epmc).pdf DeepDyve Pubget Overpricing