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http://scihub22266oqcxt.onion/ C5333229!5333229 !28269932     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28269932 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       AMIA+Annu+Symp+Proc 2016 ; 2016 (?): 1737-1746 Nephropedia Template TP {{tp|p=28269932 |t=2016. Multi-Trajectory Models of Chronic Kidney Disease Progression |pdf=|usr=}}{{28269932 }} gab.com Text Multi-Trajectory Models of Chronic Kidney Disease Progression JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=28269932 #FOAvip An ever increasing number of people are affected by chronic kidney disease (CKD). A better understanding of the progression ofCKD and its complications is needed to address what is becoming a major burden for health-care systems worldwide. Utilizing a rich data set consisting of the Electronic Health Records (EHRs) of more than 33,000 patients from a leading community nephrology practice in Western Pennsylvania, we applied group-based trajectory modeling (GBTM) in order to detect patient risk groups and uncover typical progressions of CKD and related comorbidities and complications. We have found distinct risk groups with differing trajectories and are able to classify new patients into these groups with high accuracy (up to ? 90%). Our results suggest that multitrajectory modeling via GBTM can shed light on the developmental course ofCKD and the interactions between related complications. Twit Text FOAVip Multi-Trajectory Models of Chronic Kidney Disease Progression ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=28269932 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28269932 #FOAvip English Wikipedia <ref>{{Cite pmid|28269932 }}</ref> Multi-Trajectory Models of Chronic Kidney Disease Progression #MMPMID28269932 Burckhardt P ; Nagin DS ; Padman R AMIA Annu Symp Proc 2016[]; 2016 (?): 1737-1746 PMID28269932 show ga An ever increasing number of people are affected by chronic kidney disease (CKD). A better understanding of the progression ofCKD and its complications is needed to address what is becoming a major burden for health-care systems worldwide. Utilizing a rich data set consisting of the Electronic Health Records (EHRs) of more than 33,000 patients from a leading community nephrology practice in Western Pennsylvania, we applied group-based trajectory modeling (GBTM) in order to detect patient risk groups and uncover typical progressions of CKD and related comorbidities and complications. We have found distinct risk groups with differing trajectories and are able to classify new patients into these groups with high accuracy (up to ? 90%). Our results suggest that multitrajectory modeling via GBTM can shed light on the developmental course ofCKD and the interactions between related complications. |*Disease Progression [MESH] |*Electronic Health Records [MESH] |*Models, Statistical [MESH] |*Renal Insufficiency, Chronic/physiopathology [MESH] |Datasets as Topic [MESH] |Glomerular Filtration Rate [MESH] |Humans [MESH] |Models, Biological [MESH]Multi-Trajectory Models of Chronic Kidney Disease Progression (epmc).pdf DeepDyve Pubget Overpricing