Projects / Programmes
Fuzzy logic analysis of post intesive care patient's vital functions
| Code |
Science |
Field |
Subfield |
| 2.07.07 |
Engineering sciences and technologies |
Computer science and informatics |
Intelligent systems - software |
analysis of uncertain data, analysis of fuzzy data, fuzzy statistics, fuzzy logic
Researchers (4)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
17136 |
Vito Čehovin |
|
Technical associate |
2003 - 2005 |
14 |
| 2. |
13442 |
PhD Miha Mraz |
Computer science and informatics |
Head |
2003 - 2005 |
366 |
| 3. |
21406 |
MSc Damjan Oseli |
Computer science and informatics |
Researcher |
2003 - 2005 |
31 |
| 4. |
26282 |
Primož Pečar |
Computer science and informatics |
Researcher |
2005 |
47 |
Organisations (1)
Abstract
Medical staff on duty in hospitals provides high quality care for high-risk patients, especially those situated in intensive care units. Such care is due to its high level of monitoring considerably expensive for the hospital in question. The project partner is developing a mobile device, that will be monitoring the patient's vital functions analysing them and in case of a detected critical state of the patient alarm the medical staff on duty. This way nearly intensive care level of monitoring will be provided, which is, in the critical phase of the patient's transfer from the intensive care unit to the ordinary ward, of vital importance for the patient. Whith this the intensive care hospitalization time will be shortened and indirectly the cost of hospitalization reduced.
The project aplicant intends to develop the methods and algorithms, that will be able to analyze the acquired data on the mobile device (with the patient) as well as on the hospital's server (in case of detected unusuall values). The specificity of the project is in the nature of the acquired data which enter the decision making process in a uncertain, non-crisp or fuzzy form. The existing traditional mathematical and statistical methods can not be used on such data and with their use we can not implement fuzzy or uncertain reasoning, that would, in our opinion, in case of unusual acquired values, consider a wider range of diagnoses. The project will in the analysis of (fuzzy) data implement the uncertain (fuzzy) knowledge about the diagnosis formation also.
