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10.1016/j.patter.2020.100051 http://scihub22266oqcxt.onion/10.1016/j.patter.2020.100051 C7301790!7301790!32835307     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Patterns+(N+Y) 2020 ; 1 (4): ä Nephropedia Template TP {{tp|p=32835307|t=2020. Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations |pdf=|usr=}}{{32835307}} gab.com Text Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations JO: Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=32835307 #FOAvip Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequential representations are better phenotype ?differentiators? and predictors than the ?atemporal? EHR records. Our results also demonstrated that data representations obtained from transitive sequencing of EHR observations can present novel insights about the progression of the disease that are difficult to discern when clinical data are treated independently of the patient's history. Twit Text FOAVip Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations ????Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=32835307 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32835307 #FOAvip English Wikipedia <ref>{{Cite pmid|32835307}}</ref> Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations #MMPMID32835307 Estiri H; Strasser ZH; Klann JG; McCoy TH; Wagholikar KB; Vasey S; Castro VM; Murphy ME; Murphy SN Patterns (N Y) 2020[Jul]; 1 (4): ä PMID32835307show ga Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequential representations are better phenotype ?differentiators? and predictors than the ?atemporal? EHR records. Our results also demonstrated that data representations obtained from transitive sequencing of EHR observations can present novel insights about the progression of the disease that are difficult to discern when clinical data are treated independently of the patient's history. äTransitive Sequencing Medical Records for Mining Predictive and Interp(epmc).pdf DeepDyve Pubget Overpricing