Name
Abstract | ||
|---|---|---|
This paper gives an overview of the Decision Support System (DSS) integrated in the stroke survivor's recovery support platform. The main goal of the platform is to improve life quality and conditions of patients who survived a stroke. The role of DSS within this platform is to collect medical data, calculate health state assessments, and to provide the recommendations to help the improvement of the patient's health. The implementation of DSS utilizes various programming languages, libraries and techniques. In particular, Java, JavaScript and Python have been used, statistical and AI libraries and techniques have been used, all in a single goal of estimating and predicting the patient's health state. Three major novel features are added to the DSS: personalization, multipurpose and multi-stakeholders support, and AI processing of data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCE-Berlin47944.2019.8966220 | 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin) |
Keywords | Field | DocType |
Decision Support System,Artificial Intelligence,statistical methods | Software engineering,Computer science,Decision support system,Electronic engineering,Life quality,Java,Python (programming language),Personalization,JavaScript | Conference |
ISSN | ISBN | Citations |
2166-6814 | 978-1-7281-2775-0 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milan Vidakovic | 1 | 0 | 0.34 |
| Stefan Cosic | 2 | 0 | 0.34 |
| Ognjen Cosic | 3 | 0 | 0.34 |
| Ivan Kastelan | 4 | 41 | 14.47 |
| Gordana Velikic | 5 | 10 | 8.37 |