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.

10.12890/2020_002102 http://scihub22266oqcxt.onion/10.12890/2020_002102 33585330!7875593!33585330     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Case+Rep+Intern+Med 2020 ; 7 (12): 002102 Nephropedia Template TP {{tp|p=33585330|t=2020. First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients |pdf=|usr=}}{{33585330}} gab.com Text First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients JO: Eur J Case Rep Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=33585330 #FOAvip INTRODUCTION: We tested the MyPredi() e-platform which is dedicated to the automated, intelligent detection of situations posing a risk of decompensation in geriatric patients. OBJECTIVE: The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi() e-platform through daily use. RESULTS: The telemedicine solution took 3,552 measurements for a hospitalized patient during her stay, with an average of 237 measurements per day, and issued 32 alerts, with an average of 2 alerts per day. The main risk was heart failure which generated the most alerts (n=13). The platform had 100% sensitivity for all geriatric risks, and had very satisfactory positive and negative predictive values. CONCLUSION: The present experiment validates the technological choices, the tools and the solutions developed. LEARNING POINTS: Patients with chronic conditions can be monitored with telemedicine systems to optimise their management, particularly during the COVID-19 pandemic.The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi() e-platform, through daily use in an elderly patient.The present experiment demonstrates the relevance of the technological choices, the tools and the solutions developed. Twit Text FOAVip First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients ????Eur J Case Rep Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=33585330 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33585330 #FOAvip English Wikipedia <ref>{{Cite pmid|33585330}}</ref> First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients #MMPMID33585330 Zulfiqar AA; Vaudelle O; Hajjam M; Letourneau D; Hajjam J; Erve S; Garate Escamilla AK; Hajjam A; Andres E Eur J Case Rep Intern Med 2020[]; 7 (12): 002102 PMID33585330show ga INTRODUCTION: We tested the MyPredi() e-platform which is dedicated to the automated, intelligent detection of situations posing a risk of decompensation in geriatric patients. OBJECTIVE: The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi() e-platform through daily use. RESULTS: The telemedicine solution took 3,552 measurements for a hospitalized patient during her stay, with an average of 237 measurements per day, and issued 32 alerts, with an average of 2 alerts per day. The main risk was heart failure which generated the most alerts (n=13). The platform had 100% sensitivity for all geriatric risks, and had very satisfactory positive and negative predictive values. CONCLUSION: The present experiment validates the technological choices, the tools and the solutions developed. LEARNING POINTS: Patients with chronic conditions can be monitored with telemedicine systems to optimise their management, particularly during the COVID-19 pandemic.The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi() e-platform, through daily use in an elderly patient.The present experiment demonstrates the relevance of the technological choices, the tools and the solutions developed. äFirst Test of an Automated Detection Platform to Identify Risk of Deco(epmc).pdf DeepDyve Pubget Overpricing