Name
Title | ||
|---|---|---|
Enhancing IoT-based critical diagnosis in emergency rooms through intelligent video surveillance |
Abstract | ||
|---|---|---|
The detection of critical patients in Emergency Departments is often a critical task, especially in situations in which the number of patients to be monitored is high with respect to the available medical personnel. To this end, IoT data analytics can provide a useful support in automatically monitoring the status of patients, and detect the most critical ones. This paper presents a knowledge representation frame-work enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency departments, and alert medical personnel. We also describe a clinical scenario related to the early treatment of sepsis in the emergency department, and show how the proposed framework can enhance the detection of such critical disease. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00096 | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Keywords | DocType | ISBN |
Decision Support Systems,Event Modeling,ICU,Emergency medicine,EBM,Frame-based framework | Conference | 978-1-7281-6610-0 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loredana Caruccio | 1 | 48 | 12.92 |
| Ornella Piazza | 2 | 0 | 0.34 |
| Giuseppe Polese | 3 | 263 | 38.68 |
| Genoveffa Tortora | 4 | 1477 | 151.59 |