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http://scihub22266oqcxt.onion/ 33936454!8075483!33936454     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       AMIA+Annu+Symp+Proc 2020 ; 2020 (ä): 793-802 Nephropedia Template TP {{tp|p=33936454|t=2020. Artificial Intelligence Decision Support for Medical Triage |pdf=|usr=}}{{33936454}} gab.com Text Artificial Intelligence Decision Support for Medical Triage JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=33936454 #FOAvip Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak. Twit Text FOAVip Artificial Intelligence Decision Support for Medical Triage ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=33936454 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33936454 #FOAvip English Wikipedia <ref>{{Cite pmid|33936454}}</ref> Artificial Intelligence Decision Support for Medical Triage #MMPMID33936454 Marchiori C; Dykeman D; Girardi I; Ivankay A; Thandiackal K; Zusag M; Giovannini A; Karpati D; Saenz H AMIA Annu Symp Proc 2020[]; 2020 (ä): 793-802 PMID33936454show ga Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak. |*Artificial Intelligence[MESH] |*Remote Consultation[MESH] |*Telemedicine[MESH] |*Triage[MESH] |Algorithms[MESH] |COVID-19/epidemiology/*prevention & control[MESH] |Decision Support Systems, Management[MESH] |Expert Systems[MESH] |Humans[MESH]Artificial Intelligence Decision Support for Medical Triage (epmc).pdf DeepDyve Pubget Overpricing