FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2554162271> ?p ?o ?g. }

• W2554162271 endingPage "8" @default.
• W2554162271 startingPage "1" @default.
• W2554162271 abstract "Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention." @default.
• W2554162271 created "2016-11-30" @default.
• W2554162271 creator A5008685777 @default.
• W2554162271 creator A5023296556 @default.
• W2554162271 creator A5053602068 @default.
• W2554162271 creator A5057656484 @default.
• W2554162271 creator A5058081424 @default.
• W2554162271 creator A5074058213 @default.
• W2554162271 creator A5074830972 @default.
• W2554162271 creator A5088280452 @default.
• W2554162271 date "2016-11-01" @default.
• W2554162271 modified "2023-09-25" @default.
• W2554162271 title "Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data" @default.
• W2554162271 cites W1540275579 @default.
• W2554162271 cites W19162607 @default.
• W2554162271 cites W1968747622 @default.
• W2554162271 cites W1971807437 @default.
• W2554162271 cites W1985373890 @default.
• W2554162271 cites W1986832438 @default.
• W2554162271 cites W1998151455 @default.
• W2554162271 cites W2001628017 @default.
• W2554162271 cites W2004782018 @default.
• W2554162271 cites W2007468778 @default.
• W2554162271 cites W2019352407 @default.
• W2554162271 cites W2021012510 @default.
• W2554162271 cites W2021711796 @default.
• W2554162271 cites W2027567562 @default.
• W2554162271 cites W2028626467 @default.
• W2554162271 cites W2030444606 @default.
• W2554162271 cites W2034977443 @default.
• W2554162271 cites W2040262721 @default.
• W2554162271 cites W2040680773 @default.
• W2554162271 cites W2068766374 @default.
• W2554162271 cites W2074677888 @default.
• W2554162271 cites W2083381858 @default.
• W2554162271 cites W2092872462 @default.
• W2554162271 cites W2099737510 @default.
• W2554162271 cites W2100603120 @default.
• W2554162271 cites W2102590607 @default.
• W2554162271 cites W2105420079 @default.
• W2554162271 cites W2105668940 @default.
• W2554162271 cites W2107143197 @default.
• W2554162271 cites W2109546786 @default.
• W2554162271 cites W2114175617 @default.
• W2554162271 cites W2120925604 @default.
• W2554162271 cites W2121593241 @default.
• W2554162271 cites W2126097863 @default.
• W2554162271 cites W2129526746 @default.
• W2554162271 cites W2129806728 @default.
• W2554162271 cites W2130515775 @default.
• W2554162271 cites W2132996843 @default.
• W2554162271 cites W2142419302 @default.
• W2554162271 cites W2148428564 @default.
• W2554162271 cites W2150996903 @default.
• W2554162271 cites W2152271789 @default.
• W2554162271 cites W2155142785 @default.
• W2554162271 cites W2158437501 @default.
• W2554162271 cites W2159675211 @default.
• W2554162271 cites W2164755477 @default.
• W2554162271 cites W2171231355 @default.
• W2554162271 cites W2195653601 @default.
• W2554162271 cites W2206453851 @default.
• W2554162271 cites W2316168022 @default.
• W2554162271 cites W2614446243 @default.
• W2554162271 cites W2414778501 @default.
• W2554162271 doi "https://doi.org/10.1016/j.artmed.2016.11.001" @default.
• W2554162271 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27964799" @default.
• W2554162271 hasPublicationYear "2016" @default.
• W2554162271 type Work @default.
• W2554162271 sameAs 2554162271 @default.
• W2554162271 citedByCount "27" @default.
• W2554162271 countsByYear W25541622712017 @default.
• W2554162271 countsByYear W25541622712018 @default.
• W2554162271 countsByYear W25541622712019 @default.
• W2554162271 countsByYear W25541622712020 @default.
• W2554162271 countsByYear W25541622712021 @default.
• W2554162271 countsByYear W25541622712022 @default.
• W2554162271 crossrefType "journal-article" @default.
• W2554162271 hasAuthorship W2554162271A5008685777 @default.
• W2554162271 hasAuthorship W2554162271A5023296556 @default.
• W2554162271 hasAuthorship W2554162271A5053602068 @default.
• W2554162271 hasAuthorship W2554162271A5057656484 @default.
• W2554162271 hasAuthorship W2554162271A5058081424 @default.
• W2554162271 hasAuthorship W2554162271A5074058213 @default.
• W2554162271 hasAuthorship W2554162271A5074830972 @default.
• W2554162271 hasAuthorship W2554162271A5088280452 @default.
• W2554162271 hasBestOaLocation W25541622711 @default.
• W2554162271 hasConcept C107673813 @default.
• W2554162271 hasConcept C119857082 @default.
• W2554162271 hasConcept C124101348 @default.
• W2554162271 hasConcept C142724271 @default.
• W2554162271 hasConcept C154945302 @default.
• W2554162271 hasConcept C160735492 @default.
• W2554162271 hasConcept C162324750 @default.
• W2554162271 hasConcept C207201462 @default.
• W2554162271 hasConcept C2426938 @default.
• W2554162271 hasConcept C2522767166 @default.
• W2554162271 hasConcept C33724603 @default.

• next