Matches in SemOpenAlex for { <https://semopenalex.org/work/W2887748949> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W2887748949 endingPage "206" @default.
- W2887748949 startingPage "200" @default.
- W2887748949 abstract "To examine the accuracy of machine learning to relate particulate matter (PM) 2.5 and PM10 concentrations to upper respiratory tract infections (URIs).Daily nationwide and regional outdoor PM2.5 and PM10 concentrations collected over 30 consecutive days obtained from the Taiwan Environment Protection Administration were the inputs for machine learning, using multilayer perceptron (MLP), to relate to the subsequent one-week outpatient visits for URIs. The URI data were obtained from the Centers for Disease Control datasets in Taiwan between 2009 and 2016. The testing used the middle month dataset of each season (January, April, July and October), and the training used the other months' datasets. The weekly URI cases were classified by tertile as high, moderate, and low volumes.Both PM concentrations and URI cases peak in winter and spring. In the nationwide data analysis, MLP machine learning can accurately relate the URI volumes of the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively) with the PM2.5 and PM10 concentrations. In the regional data analyses, greater accuracy is found for PM2.5 than for PM10 for the elderly, particularly in the Central region (78.10% and 74.45%, respectively), whereas greater accuracy is found for PM10 than for PM2.5 for the overall population, particularly in the Northern region (73.19% and 63.04%, respectively).Short-term PM2.5 and PM10 concentrations were accurately related to the subsequent occurrence of URIs by using machine learning. Our findings suggested that the effects of PM2.5 and PM10 on URI may differ by age, and the mechanism needs further evaluation." @default.
- W2887748949 created "2018-08-22" @default.
- W2887748949 creator A5008844615 @default.
- W2887748949 creator A5062369912 @default.
- W2887748949 creator A5063160640 @default.
- W2887748949 creator A5069031304 @default.
- W2887748949 creator A5070802972 @default.
- W2887748949 creator A5077467918 @default.
- W2887748949 creator A5088866678 @default.
- W2887748949 date "2018-08-16" @default.
- W2887748949 modified "2023-09-27" @default.
- W2887748949 title "Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis" @default.
- W2887748949 cites W2013420782 @default.
- W2887748949 cites W2014150292 @default.
- W2887748949 cites W2085784285 @default.
- W2887748949 cites W2098294355 @default.
- W2887748949 cites W2152393165 @default.
- W2887748949 cites W2163063197 @default.
- W2887748949 cites W2301541953 @default.
- W2887748949 cites W2557738935 @default.
- W2887748949 cites W2568477110 @default.
- W2887748949 cites W2598442119 @default.
- W2887748949 cites W2743269518 @default.
- W2887748949 cites W2754581087 @default.
- W2887748949 cites W2765373520 @default.
- W2887748949 cites W2772246530 @default.
- W2887748949 cites W2787703065 @default.
- W2887748949 cites W2790958806 @default.
- W2887748949 cites W2892133319 @default.
- W2887748949 doi "https://doi.org/10.12998/wjcc.v6.i8.200" @default.
- W2887748949 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6107525" @default.
- W2887748949 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30148148" @default.
- W2887748949 hasPublicationYear "2018" @default.
- W2887748949 type Work @default.
- W2887748949 sameAs 2887748949 @default.
- W2887748949 citedByCount "20" @default.
- W2887748949 countsByYear W28877489492019 @default.
- W2887748949 countsByYear W28877489492020 @default.
- W2887748949 countsByYear W28877489492021 @default.
- W2887748949 countsByYear W28877489492022 @default.
- W2887748949 countsByYear W28877489492023 @default.
- W2887748949 crossrefType "journal-article" @default.
- W2887748949 hasAuthorship W2887748949A5008844615 @default.
- W2887748949 hasAuthorship W2887748949A5062369912 @default.
- W2887748949 hasAuthorship W2887748949A5063160640 @default.
- W2887748949 hasAuthorship W2887748949A5069031304 @default.
- W2887748949 hasAuthorship W2887748949A5070802972 @default.
- W2887748949 hasAuthorship W2887748949A5077467918 @default.
- W2887748949 hasAuthorship W2887748949A5088866678 @default.
- W2887748949 hasBestOaLocation W28877489491 @default.
- W2887748949 hasConcept C119857082 @default.
- W2887748949 hasConcept C126322002 @default.
- W2887748949 hasConcept C154945302 @default.
- W2887748949 hasConcept C179717631 @default.
- W2887748949 hasConcept C2776012195 @default.
- W2887748949 hasConcept C2780805593 @default.
- W2887748949 hasConcept C2908647359 @default.
- W2887748949 hasConcept C3020110884 @default.
- W2887748949 hasConcept C41008148 @default.
- W2887748949 hasConcept C50644808 @default.
- W2887748949 hasConcept C534529494 @default.
- W2887748949 hasConcept C71924100 @default.
- W2887748949 hasConcept C99454951 @default.
- W2887748949 hasConceptScore W2887748949C119857082 @default.
- W2887748949 hasConceptScore W2887748949C126322002 @default.
- W2887748949 hasConceptScore W2887748949C154945302 @default.
- W2887748949 hasConceptScore W2887748949C179717631 @default.
- W2887748949 hasConceptScore W2887748949C2776012195 @default.
- W2887748949 hasConceptScore W2887748949C2780805593 @default.
- W2887748949 hasConceptScore W2887748949C2908647359 @default.
- W2887748949 hasConceptScore W2887748949C3020110884 @default.
- W2887748949 hasConceptScore W2887748949C41008148 @default.
- W2887748949 hasConceptScore W2887748949C50644808 @default.
- W2887748949 hasConceptScore W2887748949C534529494 @default.
- W2887748949 hasConceptScore W2887748949C71924100 @default.
- W2887748949 hasConceptScore W2887748949C99454951 @default.
- W2887748949 hasIssue "8" @default.
- W2887748949 hasLocation W28877489491 @default.
- W2887748949 hasLocation W28877489492 @default.
- W2887748949 hasLocation W28877489493 @default.
- W2887748949 hasLocation W28877489494 @default.
- W2887748949 hasOpenAccess W2887748949 @default.
- W2887748949 hasPrimaryLocation W28877489491 @default.
- W2887748949 hasRelatedWork W2787191226 @default.
- W2887748949 hasRelatedWork W2941320171 @default.
- W2887748949 hasRelatedWork W3018959556 @default.
- W2887748949 hasRelatedWork W3185179407 @default.
- W2887748949 hasRelatedWork W3211546796 @default.
- W2887748949 hasRelatedWork W4231994957 @default.
- W2887748949 hasRelatedWork W4285741730 @default.
- W2887748949 hasRelatedWork W4294067781 @default.
- W2887748949 hasRelatedWork W4320802194 @default.
- W2887748949 hasRelatedWork W4361795583 @default.
- W2887748949 hasVolume "6" @default.
- W2887748949 isParatext "false" @default.
- W2887748949 isRetracted "false" @default.
- W2887748949 magId "2887748949" @default.
- W2887748949 workType "article" @default.