It saves encoding time and errors from healthcare professionals and automatically provides data needed for a personalized clinical nutrition follow-up, like in Nutrow.
Reliable and easy to use medical device to automatically get nutrition pumps data available for a better clinical nutrition management.įeedim is the association of a light-weight and easy to use hardware device that captures data from feeding pumps and a software to continuously monitor products fed to each patient.
The system integrates malnutrition risk alerts from Scorso and integrates data coming from the EHR and Feedim to eliminate encoding times and errors, take earlier preventive actions and develop a tailored follow-up of patients. The software allows a doctor or dietician to compute patients nutritional needs, simulate optimal intakes, define a customized nutrition plan and monitor it thanks to clear and simple nutrition status dashboards or precise real-time follow-up. Nutrow is the central clinical nutrition cockpit that gathers scattered clinical data and combines them with intuitive user interfaces and scientific algorithms to implement a 360° management of your patients nutrition. The first complete clinical nutrition management system for the optimization of your patients nutritional status from hospital to home. Patient history follow-up is built-in and alarms can be configured to warn medical staff in charge and directly appear in Nutrow.
The software is straightforward to use on a mobile device or a PC and connects to scales, smartwatches or even lab results for easier data entry. Scorso is a family of scoring procedures that gathers all the widely used scientific malnutrition scoring procedures like NRS2002, MNA, SGA… and a set of specific assessments to quickly detect and address risks linked to nutrition. Portfolio of easy to use scientific scorings of nutrition risks for a closer follow-up and a faster patient care. Product category: eHealth, telemedicine / telematics / telemetry With this chapter, the authors aim to equip students with the necessary skills to undertake graduate-level courses on GPU programming and make a strong start with undergraduate research.06 Information and Communication TechnologyĠ6.04 eHealth, telemedicine / telematics / telemetryĮHealth, telemedicine / telematics / telemetry The authors believe that the chapter layout facilitates effective student-learning by starting from the basics of GPGPU computing and then leading up to the advanced concepts.
The chapter sections include: (1) Data parallelism (2) CUDA program structure (3) CUDA compilation flow (4) CUDA thread organization (5) Kernel: Execution configuration and kernel structure (6) CUDA memory organization (7) CUDA optimizations (8) Case study: Image convolution on GPUs and (9) GPU computing: The future. The chapter opens with an introduction to GPGPU computing. The chapter consists of nine pedagogical sections with several active-learning exercises to effectively engage students with the text. The specific focus of the chapter is on GPGPU computing using the Compute Unified Device Architecture (CUDA) C framework due to the following three reasons: (1) Nvidia GPUs are ubiquitous in high-performance computing, (2) CUDA is relatively easy to understand versus OpenCL, especially for UG students with limited heterogeneous device programming experience, and (3) CUDA experience simplifies learning OpenCL and OpenACC. The goal of the chapter is to introduce the upper-level Computer Engineering/Computer Science undergraduate (UG) students to general-purpose graphical processing unit (GPGPU) computing.