Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1186/s12872-021-01925-7 http://scihub22266oqcxt.onion/10.1186/s12872-021-01925-7 33750304!7941334!33750304     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Cardiovasc+Disord 2021 ; 21 (1): 129 Nephropedia Template TP {{tp|p=33750304|t=2021. Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease |pdf=|usr=}}{{33750304}} gab.com Text Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease JO: BMC Cardiovasc Disord http://www.kidney.de/pget.php?&tw=1&pmid=33750304 #FOAvip BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination METHODS: Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and >/= 65 age groups. RESULTS: The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the >/= 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). CONCLUSIONS: The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination. Twit Text FOAVip Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease ????BMC Cardiovasc Disord http://www.kidney.de/pget.php?&tw=1&pmid=33750304 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33750304 #FOAvip English Wikipedia <ref>{{Cite pmid|33750304}}</ref> Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease #MMPMID33750304 Kim M; Kim YJ; Park SJ; Kim KG; Oh PC; Kim YS; Kim EY BMC Cardiovasc Disord 2021[Mar]; 21 (1): 129 PMID33750304show ga BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination METHODS: Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and >/= 65 age groups. RESULTS: The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the >/= 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). CONCLUSIONS: The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination. |*Medication Adherence[MESH] |*Support Vector Machine[MESH] |*Vaccination[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |Cardiovascular Diseases/diagnosis/*epidemiology[MESH] |Cross-Sectional Studies[MESH] |Databases, Factual[MESH] |Female[MESH] |Humans[MESH] |Influenza Vaccines/*administration & dosage[MESH] |Influenza, Human/diagnosis/epidemiology/*prevention & control[MESH] |Male[MESH] |Middle Aged[MESH] |Nutrition Surveys[MESH] |Republic of Korea/epidemiology[MESH] |Risk Assessment[MESH] |Risk Factors[MESH]Machine learning models to identify low adherence to influenza vaccina(epmc).pdf DeepDyve Pubget Overpricing