Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765200097> ?p ?o ?g. }
Showing items 1 to 52 of
52
with 100 items per page.
- W2765200097 endingPage "10997" @default.
- W2765200097 startingPage "10992" @default.
- W2765200097 abstract "Abstract Health status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then take appropriate treatment, which is of great clinical significance. In this study, a data-driven learning approach called LWPR-PCA is first applied to monitor health status of patients in intensive care unit (ICU). Locally weighted projection regression (LWPR) is used to approximate the complex nonlinear process by using local linear models, and then principal component analysis (PCA) is further applied to status monitoring. LWPR-PCA is a good candidate to establish an individual-type model for an ICU patient and to improve the global monitoring performance, which is the mainstream direction of modern medicine. To confirm the superiority of LWPR-PCA, physiological data of 18 ICU patients are collected, of which the mean fault detection rates (FDRs) are increased by 4.8% and 4.6%, and the mean fault alarm rates (FAR) are decreased by 6.7% and 5.9% in terms of two kinds of faults, compared to the latest reported method L-PCA, which combines just in time learning and modified PCA methods." @default.
- W2765200097 created "2017-11-10" @default.
- W2765200097 creator A5044233054 @default.
- W2765200097 creator A5074977317 @default.
- W2765200097 date "2017-07-01" @default.
- W2765200097 modified "2023-09-23" @default.
- W2765200097 title "Health Status Monitoring for ICU Patients Based on LWPR-PCA" @default.
- W2765200097 cites W1976606095 @default.
- W2765200097 cites W1997545358 @default.
- W2765200097 cites W2000576329 @default.
- W2765200097 cites W2003376257 @default.
- W2765200097 cites W2046788142 @default.
- W2765200097 cites W2078426082 @default.
- W2765200097 cites W2137805911 @default.
- W2765200097 cites W2161687618 @default.
- W2765200097 cites W2170835861 @default.
- W2765200097 cites W2410570928 @default.
- W2765200097 doi "https://doi.org/10.1016/j.ifacol.2017.08.2474" @default.
- W2765200097 hasPublicationYear "2017" @default.
- W2765200097 type Work @default.
- W2765200097 sameAs 2765200097 @default.
- W2765200097 citedByCount "1" @default.
- W2765200097 countsByYear W27652000972019 @default.
- W2765200097 crossrefType "journal-article" @default.
- W2765200097 hasAuthorship W2765200097A5044233054 @default.
- W2765200097 hasAuthorship W2765200097A5074977317 @default.
- W2765200097 hasBestOaLocation W27652000971 @default.
- W2765200097 hasConcept C194828623 @default.
- W2765200097 hasConcept C71924100 @default.
- W2765200097 hasConceptScore W2765200097C194828623 @default.
- W2765200097 hasConceptScore W2765200097C71924100 @default.
- W2765200097 hasIssue "1" @default.
- W2765200097 hasLocation W27652000971 @default.
- W2765200097 hasOpenAccess W2765200097 @default.
- W2765200097 hasPrimaryLocation W27652000971 @default.
- W2765200097 hasRelatedWork W159247329 @default.
- W2765200097 hasRelatedWork W1967343366 @default.
- W2765200097 hasRelatedWork W1973403174 @default.
- W2765200097 hasRelatedWork W2048982843 @default.
- W2765200097 hasRelatedWork W2083479052 @default.
- W2765200097 hasRelatedWork W2351161810 @default.
- W2765200097 hasRelatedWork W2510700473 @default.
- W2765200097 hasRelatedWork W2531873760 @default.
- W2765200097 hasRelatedWork W2613637409 @default.
- W2765200097 hasRelatedWork W2626766173 @default.
- W2765200097 hasVolume "50" @default.
- W2765200097 isParatext "false" @default.
- W2765200097 isRetracted "false" @default.
- W2765200097 magId "2765200097" @default.
- W2765200097 workType "article" @default.