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10.1145/3097983.3097998 http://scihub22266oqcxt.onion/10.1145/3097983.3097998 C5945223!5945223!29755826     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29755826&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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       KDD 2017 ; 2017 (ä): 1537-46 Nephropedia Template TP {{tp|p=29755826|t=2017. Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data |pdf=|usr=}}{{29755826}} gab.com Text Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data JO: KDD http://www.kidney.de/pget.php?&tw=1&pmid=29755826 #FOAvip Several prominent public health hazards [29] that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV) [6,7], the science and activities to monitor and prevent adverse events caused by pharmaceutical products after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) [6] such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Self-Controlled Case Series (SCCS) model [27], arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times. We apply the proposed method as well as SCCS to the Marshfield Clinic EHR. Experimental results suggest that the proposed method outperforms SCCS under various settings in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth [26]. Twit Text FOAVip Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data ????KDD http://www.kidney.de/pget.php?&tw=1&pmid=29755826 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29755826 #FOAvip English Wikipedia <ref>{{Cite pmid|29755826}}</ref> Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data #MMPMID29755826 Kuang Z; Peissig P; Costa VS; Maclin R; Page D KDD 2017[Aug]; 2017 (ä): 1537-46 PMID29755826show ga Several prominent public health hazards [29] that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV) [6,7], the science and activities to monitor and prevent adverse events caused by pharmaceutical products after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) [6] such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Self-Controlled Case Series (SCCS) model [27], arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times. We apply the proposed method as well as SCCS to the Marshfield Clinic EHR. Experimental results suggest that the proposed method outperforms SCCS under various settings in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth [26]. äPharmacovigilance via Baseline Regularization with Large-Scale Longitu(epmc).pdf DeepDyve Pubget Overpricing