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10.1093/ajcp/aqab187 http://scihub22266oqcxt.onion/10.1093/ajcp/aqab187 34791032!8690000!34791032     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Clin+Pathol 2022 ; 157 (5): 758-766 Nephropedia Template TP {{tp|p=34791032|t=2022. Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests |pdf=|usr=}}{{34791032}} gab.com Text Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests JO: Am J Clin Pathol http://www.kidney.de/pget.php?&tw=1&pmid=34791032 #FOAvip OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses. Twit Text FOAVip Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests ????Am J Clin Pathol http://www.kidney.de/pget.php?&tw=1&pmid=34791032 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34791032 #FOAvip English Wikipedia <ref>{{Cite pmid|34791032}}</ref> Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests #MMPMID34791032 Cubukcu HC; Topcu DI; Bayraktar N; Gulsen M; Sari N; Arslan AH Am J Clin Pathol 2022[May]; 157 (5): 758-766 PMID34791032show ga OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses. |*COVID-19/diagnosis[MESH] |Algorithms[MESH] |Humans[MESH] |Logistic Models[MESH] |Machine Learning[MESH]Detection of COVID-19 by Machine Learning Using Routine Laboratory Tes(epmc).pdf DeepDyve Pubget Overpricing