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10.1371/journal.pone.0199839 http://scihub22266oqcxt.onion/10.1371/journal.pone.0199839 C6053162!6053162!30024918     free     free     free 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       PLoS+One 2018 ; 13 (7): ä Nephropedia Template TP {{tp|p=30024918|t=2018. Predicting acute kidney injury in cancer patients using heterogeneous and irregular data |pdf=|usr=}}{{30024918}} gab.com Text Predicting acute kidney injury in cancer patients using heterogeneous and irregular data JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30024918 #FOAvip How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. Twit Text FOAVip Predicting acute kidney injury in cancer patients using heterogeneous and irregular data ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=30024918 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30024918 #FOAvip English Wikipedia <ref>{{Cite pmid|30024918}}</ref> Predicting acute kidney injury in cancer patients using heterogeneous and irregular data #MMPMID30024918 Park N; Kang E; Park M; Lee H; Kang HG; Yoon HJ; Kang U PLoS One 2018[]; 13 (7): ä PMID30024918show ga How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. äPredicting acute kidney injury in cancer patients using heterogeneous (epmc).pdf DeepDyve Pubget Overpricing