FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3023164562> ?p ?o ?g. }

• W3023164562 abstract "Abstract Background Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension. Method We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discrete-feature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/area-under-curve analyses were used to compare performance. Results The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [ p < 0.001]), at least at the implementation level of a clinically relevant prediction algorithm, may be superior to a discrete feature quantification approach (AUC = 0.87 [ p < 0.001]), providing implicit access to a plurality of features and combinations thereof. In addition, the expansion of the approach to include the submission of other physiological data signals, such as heart rate variability, to the network can be readily envisioned. Conclusion This pilot study has demonstrated that increased coherence in Arterial Stiffness (AS) variability obtained from the pulse wave analysis of a continuous non-invasive blood pressure device appears to be an effective predictor of hypotension after spinal anesthesia in the obstetrics population undergoing C/S. This allowed us to predict specific dosing thresholds of phenylephrine required to maintain systolic blood pressure above 90 mmHg." @default.
• W3023164562 created "2020-05-13" @default.
• W3023164562 creator A5009031703 @default.
• W3023164562 creator A5047209210 @default.
• W3023164562 creator A5052073762 @default.
• W3023164562 creator A5053558541 @default.
• W3023164562 creator A5057805510 @default.
• W3023164562 creator A5059512015 @default.
• W3023164562 creator A5080595430 @default.
• W3023164562 date "2020-05-01" @default.
• W3023164562 modified "2023-10-07" @default.
• W3023164562 title "The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)" @default.
• W3023164562 cites W1530409884 @default.
• W3023164562 cites W1995706219 @default.
• W3023164562 cites W2016300113 @default.
• W3023164562 cites W2022183999 @default.
• W3023164562 cites W2028898118 @default.
• W3023164562 cites W2031192389 @default.
• W3023164562 cites W2048090345 @default.
• W3023164562 cites W2060783263 @default.
• W3023164562 cites W2068597752 @default.
• W3023164562 cites W2079781837 @default.
• W3023164562 cites W2116089703 @default.
• W3023164562 cites W2120438460 @default.
• W3023164562 cites W2137483363 @default.
• W3023164562 cites W2142823558 @default.
• W3023164562 cites W2148496654 @default.
• W3023164562 cites W2152608926 @default.
• W3023164562 cites W2159094171 @default.
• W3023164562 cites W2398059493 @default.
• W3023164562 cites W2569194012 @default.
• W3023164562 cites W2598582739 @default.
• W3023164562 cites W2603619573 @default.
• W3023164562 cites W2685749953 @default.
• W3023164562 cites W2762254164 @default.
• W3023164562 cites W2765221499 @default.
• W3023164562 cites W2766698488 @default.
• W3023164562 cites W2788915030 @default.
• W3023164562 cites W2803378358 @default.
• W3023164562 cites W2808701318 @default.
• W3023164562 cites W2928352011 @default.
• W3023164562 cites W2969269152 @default.
• W3023164562 cites W2994005959 @default.
• W3023164562 doi "https://doi.org/10.1186/s12871-020-01015-9" @default.
• W3023164562 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7195764" @default.
• W3023164562 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32357833" @default.
• W3023164562 hasPublicationYear "2020" @default.
• W3023164562 type Work @default.
• W3023164562 sameAs 3023164562 @default.
• W3023164562 citedByCount "4" @default.
• W3023164562 countsByYear W30231645622021 @default.
• W3023164562 countsByYear W30231645622022 @default.
• W3023164562 countsByYear W30231645622023 @default.
• W3023164562 crossrefType "journal-article" @default.
• W3023164562 hasAuthorship W3023164562A5009031703 @default.
• W3023164562 hasAuthorship W3023164562A5047209210 @default.
• W3023164562 hasAuthorship W3023164562A5052073762 @default.
• W3023164562 hasAuthorship W3023164562A5053558541 @default.
• W3023164562 hasAuthorship W3023164562A5057805510 @default.
• W3023164562 hasAuthorship W3023164562A5059512015 @default.
• W3023164562 hasAuthorship W3023164562A5080595430 @default.
• W3023164562 hasBestOaLocation W30231645621 @default.
• W3023164562 hasConcept C126322002 @default.
• W3023164562 hasConcept C154945302 @default.
• W3023164562 hasConcept C178853913 @default.
• W3023164562 hasConcept C2779526319 @default.
• W3023164562 hasConcept C41008148 @default.
• W3023164562 hasConcept C42219234 @default.
• W3023164562 hasConcept C50644808 @default.
• W3023164562 hasConcept C58471807 @default.
• W3023164562 hasConcept C71924100 @default.
• W3023164562 hasConcept C84393581 @default.
• W3023164562 hasConceptScore W3023164562C126322002 @default.
• W3023164562 hasConceptScore W3023164562C154945302 @default.
• W3023164562 hasConceptScore W3023164562C178853913 @default.
• W3023164562 hasConceptScore W3023164562C2779526319 @default.
• W3023164562 hasConceptScore W3023164562C41008148 @default.
• W3023164562 hasConceptScore W3023164562C42219234 @default.
• W3023164562 hasConceptScore W3023164562C50644808 @default.
• W3023164562 hasConceptScore W3023164562C58471807 @default.
• W3023164562 hasConceptScore W3023164562C71924100 @default.
• W3023164562 hasConceptScore W3023164562C84393581 @default.
• W3023164562 hasIssue "1" @default.
• W3023164562 hasLocation W30231645621 @default.
• W3023164562 hasLocation W30231645622 @default.
• W3023164562 hasLocation W30231645623 @default.
• W3023164562 hasLocation W30231645624 @default.
• W3023164562 hasOpenAccess W3023164562 @default.
• W3023164562 hasPrimaryLocation W30231645621 @default.
• W3023164562 hasRelatedWork W1996522485 @default.
• W3023164562 hasRelatedWork W2037868453 @default.
• W3023164562 hasRelatedWork W2051221410 @default.
• W3023164562 hasRelatedWork W2054271519 @default.
• W3023164562 hasRelatedWork W2358112737 @default.
• W3023164562 hasRelatedWork W2370814053 @default.
• W3023164562 hasRelatedWork W2392249173 @default.
• W3023164562 hasRelatedWork W2404363966 @default.
• W3023164562 hasRelatedWork W2549135832 @default.
• W3023164562 hasRelatedWork W2986581864 @default.
• W3023164562 hasVolume "20" @default.

• next