NEFROCLOUD
A cloud service supporting predictive medicine for haemodialysis patients
] NefroCloud develops a cloud-based software service grounded in predictive medicine to support the management of haemodialysis patients, integrating clinical data and predictive algorithms to monitor dialysis efficiency and support timely interventions by healthcare operators.
The project
] NefroCloud is a Research and Development project aimed at creating a cloud-delivered software service to support the management of patients on haemodialysis treatment. ] The project arises from the need to improve dialysis efficiency, a determining factor for patients' quality of life and for the reduction of clinical complications and associated healthcare costs. The system acquires clinical data and information from haemodialysis devices and healthcare operators, integrating them into a digital platform for the analysis and monitoring of treatment. Through machine-learning algorithms, NefroCloud will make it possible to identify early changes in dialysis efficiency and to support clinical decisions.
Results
The objective of the NefroCloud project is to develop a digital service based on predictive medicine and Artificial Intelligence to improve the monitoring and management of haemodialysis patients. The specific objectives include: automating the acquisition of data from dialysis devices and healthcare operators; creating a cloud platform for the storage and processing of clinical information; developing Machine Learning algorithms for predicting trends in dialysis efficiency; and implementing early-warning systems to support healthcare operators in the timely identification of at-risk conditions and to improve treatment personalisation The NefroCloud project enabled the creation of an integrated platform for the acquisition, processing and analysis of the clinical data of haemodialysis patients.
The results achieved include the development of a cloud system for the centralised management of clinical information, the implementation of predictive algorithms for the early identification of changes in dialysis efficiency and the creation of dedicated interfaces for healthcare operators for data visualisation and the receipt of automatic alerts.
The project also contributed to improving clinical-monitoring tools and to consolidating the use of innovative digital approaches to support the therapeutic management of the haemodialysis patient.
