Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324133524> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4324133524 abstract "Abstract Transformer is the latest deep neural network (DNN) architecture for sequence data learning that has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain. Despite the similarities between longitudinal clinical data and natural language data, clinical data presents unique complexities that make adapting Transformer to this domain challenging. To address this issue, we have designed a new Transformer-based DNN architecture, referred to as Hybrid Value-Aware Transformer (HVAT), which can jointly learn from longitudinal and non-longitudinal clinical data. HVAT is unique in the ability to learn from the numerical values associated with clinical codes/concepts such as labs, and also the use of a flexible longitudinal data representation called clinical tokens. We trained a prototype HVAT model on a case-control dataset, achieving high performance in predicting Alzheimer’s disease and related dementias as the patient outcome. The result demonstrates the potential of HVAT for broader clinical data learning tasks." @default.
- W4324133524 created "2023-03-15" @default.
- W4324133524 creator A5006718110 @default.
- W4324133524 creator A5030873899 @default.
- W4324133524 creator A5037161166 @default.
- W4324133524 creator A5048736829 @default.
- W4324133524 creator A5058747294 @default.
- W4324133524 creator A5084025490 @default.
- W4324133524 creator A5090492562 @default.
- W4324133524 date "2023-03-14" @default.
- W4324133524 modified "2023-09-27" @default.
- W4324133524 title "Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data" @default.
- W4324133524 cites W1677182931 @default.
- W4324133524 cites W1974215045 @default.
- W4324133524 cites W2194775991 @default.
- W4324133524 cites W2510560885 @default.
- W4324133524 cites W2514071032 @default.
- W4324133524 cites W2939903565 @default.
- W4324133524 cites W2963716420 @default.
- W4324133524 cites W3017637887 @default.
- W4324133524 cites W3042022206 @default.
- W4324133524 cites W3160137267 @default.
- W4324133524 cites W4353018831 @default.
- W4324133524 doi "https://doi.org/10.1101/2023.03.09.23287046" @default.
- W4324133524 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36993767" @default.
- W4324133524 hasPublicationYear "2023" @default.
- W4324133524 type Work @default.
- W4324133524 citedByCount "1" @default.
- W4324133524 countsByYear W43241335242023 @default.
- W4324133524 crossrefType "posted-content" @default.
- W4324133524 hasAuthorship W4324133524A5006718110 @default.
- W4324133524 hasAuthorship W4324133524A5030873899 @default.
- W4324133524 hasAuthorship W4324133524A5037161166 @default.
- W4324133524 hasAuthorship W4324133524A5048736829 @default.
- W4324133524 hasAuthorship W4324133524A5058747294 @default.
- W4324133524 hasAuthorship W4324133524A5084025490 @default.
- W4324133524 hasAuthorship W4324133524A5090492562 @default.
- W4324133524 hasBestOaLocation W43241335241 @default.
- W4324133524 hasConcept C108583219 @default.
- W4324133524 hasConcept C115260700 @default.
- W4324133524 hasConcept C119599485 @default.
- W4324133524 hasConcept C119857082 @default.
- W4324133524 hasConcept C121332964 @default.
- W4324133524 hasConcept C123657996 @default.
- W4324133524 hasConcept C127413603 @default.
- W4324133524 hasConcept C142362112 @default.
- W4324133524 hasConcept C147168706 @default.
- W4324133524 hasConcept C153349607 @default.
- W4324133524 hasConcept C154945302 @default.
- W4324133524 hasConcept C165801399 @default.
- W4324133524 hasConcept C2993942811 @default.
- W4324133524 hasConcept C41008148 @default.
- W4324133524 hasConcept C50644808 @default.
- W4324133524 hasConcept C62520636 @default.
- W4324133524 hasConcept C66322947 @default.
- W4324133524 hasConceptScore W4324133524C108583219 @default.
- W4324133524 hasConceptScore W4324133524C115260700 @default.
- W4324133524 hasConceptScore W4324133524C119599485 @default.
- W4324133524 hasConceptScore W4324133524C119857082 @default.
- W4324133524 hasConceptScore W4324133524C121332964 @default.
- W4324133524 hasConceptScore W4324133524C123657996 @default.
- W4324133524 hasConceptScore W4324133524C127413603 @default.
- W4324133524 hasConceptScore W4324133524C142362112 @default.
- W4324133524 hasConceptScore W4324133524C147168706 @default.
- W4324133524 hasConceptScore W4324133524C153349607 @default.
- W4324133524 hasConceptScore W4324133524C154945302 @default.
- W4324133524 hasConceptScore W4324133524C165801399 @default.
- W4324133524 hasConceptScore W4324133524C2993942811 @default.
- W4324133524 hasConceptScore W4324133524C41008148 @default.
- W4324133524 hasConceptScore W4324133524C50644808 @default.
- W4324133524 hasConceptScore W4324133524C62520636 @default.
- W4324133524 hasConceptScore W4324133524C66322947 @default.
- W4324133524 hasLocation W43241335241 @default.
- W4324133524 hasLocation W43241335242 @default.
- W4324133524 hasLocation W43241335243 @default.
- W4324133524 hasOpenAccess W4324133524 @default.
- W4324133524 hasPrimaryLocation W43241335241 @default.
- W4324133524 hasRelatedWork W3014300295 @default.
- W4324133524 hasRelatedWork W3164822677 @default.
- W4324133524 hasRelatedWork W4223943233 @default.
- W4324133524 hasRelatedWork W4225161397 @default.
- W4324133524 hasRelatedWork W4250304930 @default.
- W4324133524 hasRelatedWork W4312200629 @default.
- W4324133524 hasRelatedWork W4360585206 @default.
- W4324133524 hasRelatedWork W4364306694 @default.
- W4324133524 hasRelatedWork W4380075502 @default.
- W4324133524 hasRelatedWork W4380086463 @default.
- W4324133524 isParatext "false" @default.
- W4324133524 isRetracted "false" @default.
- W4324133524 workType "article" @default.