FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309494803> ?p ?o ?g. }

• W4309494803 endingPage "e0272919" @default.
• W4309494803 startingPage "e0272919" @default.
• W4309494803 abstract "Hospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral infections, and subsequently higher rates of morbidity and mortality.We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets, including individual-level factors, engineering control factors, and administrative control factors. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies.Regarding the individual-level risk model, the variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California's higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level.The accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies." @default.
• W4309494803 created "2022-11-28" @default.
• W4309494803 creator A5036866260 @default.
• W4309494803 creator A5037243915 @default.
• W4309494803 creator A5051973944 @default.
• W4309494803 creator A5053781401 @default.
• W4309494803 creator A5059821783 @default.
• W4309494803 creator A5072185942 @default.
• W4309494803 date "2022-11-21" @default.
• W4309494803 modified "2023-09-29" @default.
• W4309494803 title "A domain-knowledge modeling of hospital-acquired infection risk in Healthcare personnel from retrospective observational data: A case study for COVID-19" @default.
• W4309494803 cites W159957110 @default.
• W4309494803 cites W1817561967 @default.
• W4309494803 cites W1985641228 @default.
• W4309494803 cites W1993795503 @default.
• W4309494803 cites W2009020381 @default.
• W4309494803 cites W2020149603 @default.
• W4309494803 cites W2046086311 @default.
• W4309494803 cites W2048859944 @default.
• W4309494803 cites W2049256850 @default.
• W4309494803 cites W2051084650 @default.
• W4309494803 cites W2084489460 @default.
• W4309494803 cites W2104073679 @default.
• W4309494803 cites W2111280832 @default.
• W4309494803 cites W2119560885 @default.
• W4309494803 cites W2138723883 @default.
• W4309494803 cites W2139495090 @default.
• W4309494803 cites W2146036952 @default.
• W4309494803 cites W2148502851 @default.
• W4309494803 cites W2173586263 @default.
• W4309494803 cites W2471055439 @default.
• W4309494803 cites W2530145764 @default.
• W4309494803 cites W2545004184 @default.
• W4309494803 cites W2982453601 @default.
• W4309494803 cites W3009926341 @default.
• W4309494803 cites W3013233995 @default.
• W4309494803 cites W3014199470 @default.
• W4309494803 cites W3015401053 @default.
• W4309494803 cites W3016097752 @default.
• W4309494803 cites W3017430478 @default.
• W4309494803 cites W3021112975 @default.
• W4309494803 cites W3021442355 @default.
• W4309494803 cites W3024594827 @default.
• W4309494803 cites W3032742287 @default.
• W4309494803 cites W3033609484 @default.
• W4309494803 cites W3046353798 @default.
• W4309494803 cites W3046400528 @default.
• W4309494803 cites W3081411221 @default.
• W4309494803 cites W3085331172 @default.
• W4309494803 cites W3089127032 @default.
• W4309494803 cites W3095993761 @default.
• W4309494803 cites W3105116956 @default.
• W4309494803 cites W3111513098 @default.
• W4309494803 cites W3126771207 @default.
• W4309494803 cites W3127941983 @default.
• W4309494803 cites W3136304122 @default.
• W4309494803 cites W3140110652 @default.
• W4309494803 cites W3205029301 @default.
• W4309494803 doi "https://doi.org/10.1371/journal.pone.0272919" @default.
• W4309494803 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36409727" @default.
• W4309494803 hasPublicationYear "2022" @default.
• W4309494803 type Work @default.
• W4309494803 citedByCount "1" @default.
• W4309494803 countsByYear W43094948032023 @default.
• W4309494803 crossrefType "journal-article" @default.
• W4309494803 hasAuthorship W4309494803A5036866260 @default.
• W4309494803 hasAuthorship W4309494803A5037243915 @default.
• W4309494803 hasAuthorship W4309494803A5051973944 @default.
• W4309494803 hasAuthorship W4309494803A5053781401 @default.
• W4309494803 hasAuthorship W4309494803A5059821783 @default.
• W4309494803 hasAuthorship W4309494803A5072185942 @default.
• W4309494803 hasBestOaLocation W43094948031 @default.
• W4309494803 hasConcept C126322002 @default.
• W4309494803 hasConcept C151956035 @default.
• W4309494803 hasConcept C160735492 @default.
• W4309494803 hasConcept C162324750 @default.
• W4309494803 hasConcept C166888038 @default.
• W4309494803 hasConcept C177713679 @default.
• W4309494803 hasConcept C194828623 @default.
• W4309494803 hasConcept C23131810 @default.
• W4309494803 hasConcept C2908647359 @default.
• W4309494803 hasConcept C41008148 @default.
• W4309494803 hasConcept C50522688 @default.
• W4309494803 hasConcept C71924100 @default.
• W4309494803 hasConcept C761482 @default.
• W4309494803 hasConcept C76155785 @default.
• W4309494803 hasConcept C99454951 @default.
• W4309494803 hasConceptScore W4309494803C126322002 @default.
• W4309494803 hasConceptScore W4309494803C151956035 @default.
• W4309494803 hasConceptScore W4309494803C160735492 @default.
• W4309494803 hasConceptScore W4309494803C162324750 @default.
• W4309494803 hasConceptScore W4309494803C166888038 @default.
• W4309494803 hasConceptScore W4309494803C177713679 @default.
• W4309494803 hasConceptScore W4309494803C194828623 @default.
• W4309494803 hasConceptScore W4309494803C23131810 @default.
• W4309494803 hasConceptScore W4309494803C2908647359 @default.
• W4309494803 hasConceptScore W4309494803C41008148 @default.
• W4309494803 hasConceptScore W4309494803C50522688 @default.

• next