Monitoring vital signs using fuzzy logic rules
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Khorozovn O. A., Krak Iurii V., Kulias A. I., Kasianiuk Veda S., Wójcik Waldemar, Tergeusizova Aliya |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 237 - 245 |
| Scopus® Cytowania: | 5 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The methods of machine learning for real-time detection of abnormal values of the patient’s vital signs are considered. The aim is to assess the risk of the disease with worsening of the patient’s condition. The system is designed to monitor patients using expert assessments that are included in fuzzy logic rules to compare patient vital signs with disease risk assessment. Deviation of values from the norm is identified as an “abnormal” class in order to determine the reasons for the worsening of the patient’s condition. The integrated platform “m-Health” system for decision making with feedback control allows the patient to be mobile and their vital signs are mapped in the current mode |
