dc.creatorNieto-Chaupis, Huber
dc.date.accessioned2024-04-08T14:36:41Z
dc.date.accessioned2024-08-06T20:56:12Z
dc.date.available2024-04-08T14:36:41Z
dc.date.available2024-08-06T20:56:12Z
dc.date.created2024-04-08T14:36:41Z
dc.date.issued2023
dc.identifierhttps://hdl.handle.net/20.500.13067/3105
dc.identifier2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9539001
dc.description.abstractThis paper presents a scheme of self-management that employs directly the theorem of Bayes to calculate realistic probabilities to experience stroke in the shortest and middle terms. In concrete the probabilities might be used in an application by which diabetic patients can carry out by themselves periodical measurements of probabilities of risk. It is emphazised the fact that Bayes rule can be powerful but used as tool to predict stroke might also to trigger false alarms or be blind to stroke.
dc.languageeng
dc.publisherIEEE
dc.relationhttps://doi.org/10.1109/ICECET58911.2023.10389192
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectStroke
dc.subjectDiabetes
dc.subjectBayes theorem
dc.subjectCholesterol
dc.titleSelf-Management to Anticipate Stroke in Diabetic Patients Through Algorithm Based on Probability of Bayes
dc.typeinfo:eu-repo/semantics/article


Este ítem pertenece a la siguiente institución