In this study, we describe an automatic classifier of patients with Heart Failure designed for a telemonitoring scenario, improving the results obtained in our previous works. Our previous studies showed that the technique that better processes the heart failure typical telemonitoring-parameters is the Classification Tree. We therefore decided to analyze the data with its direct evolution that is the Random Forest algorithm. The results show an improvement both in accuracy and in limiting critical errors.
This paper shows a method to get a patient tracking RFId solution, basing on a multilayer planning architecture. This approach is thought to guarantee that the found technical solution is as much as possible coherent to the very initial idea. Project aims, functional requirements and technical constraints are defined in order to arrive to an active RFId solution to track and identify patients inside a hospital. The article also deals with economical issues and physical design aspects. In this work it's also defined a three phases process for patient tracking, that could serve as a guideline for different technical solutions to the same problem.
Abstract-This paper presents a mobile app called "Careggi Smart Hospital" which has been developed for the Careggi Polyclinic in Florence. The application is designed for Android smartphones and tablets and it is freely downloadable from the Google Play Store. It provides various useful tools to the hospital's users such as personnel and structures finding, wayfinding and the possibility to access personal medical records collected on regional electronic health record.
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