FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3129338704> ?p ?o ?g. }

• W3129338704 endingPage "1433" @default.
• W3129338704 startingPage "1417" @default.
• W3129338704 abstract "Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient's mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers." @default.
• W3129338704 created "2021-03-01" @default.
• W3129338704 creator A5049459342 @default.
• W3129338704 creator A5049974611 @default.
• W3129338704 creator A5059927670 @default.
• W3129338704 creator A5066754863 @default.
• W3129338704 date "2021-02-18" @default.
• W3129338704 modified "2023-09-30" @default.
• W3129338704 title "Multi-layer Representation Learning and Its Application to Electronic Health Records" @default.
• W3129338704 cites W2064675550 @default.
• W3129338704 cites W2284851926 @default.
• W3129338704 cites W2396881363 @default.
• W3129338704 cites W2404901863 @default.
• W3129338704 cites W2481271618 @default.
• W3129338704 cites W2511950764 @default.
• W3129338704 cites W2523637100 @default.
• W3129338704 cites W2539353608 @default.
• W3129338704 cites W2579512741 @default.
• W3129338704 cites W2625625371 @default.
• W3129338704 cites W2727810912 @default.
• W3129338704 cites W2789456849 @default.
• W3129338704 cites W2804025582 @default.
• W3129338704 cites W2809396336 @default.
• W3129338704 cites W2907364764 @default.
• W3129338704 cites W2910093290 @default.
• W3129338704 cites W2915236254 @default.
• W3129338704 cites W2919115771 @default.
• W3129338704 cites W2920898473 @default.
• W3129338704 cites W2960514116 @default.
• W3129338704 cites W2963611386 @default.
• W3129338704 cites W2963650911 @default.
• W3129338704 cites W2965251793 @default.
• W3129338704 cites W2985962305 @default.
• W3129338704 cites W2996036738 @default.
• W3129338704 cites W3000238064 @default.
• W3129338704 cites W3000499162 @default.
• W3129338704 cites W3004952083 @default.
• W3129338704 cites W3005251548 @default.
• W3129338704 cites W3017644243 @default.
• W3129338704 cites W3021463136 @default.
• W3129338704 cites W3023252280 @default.
• W3129338704 cites W3099136959 @default.
• W3129338704 cites W3128667387 @default.
• W3129338704 doi "https://doi.org/10.1007/s11063-021-10449-2" @default.
• W3129338704 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7891814" @default.
• W3129338704 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33623481" @default.
• W3129338704 hasPublicationYear "2021" @default.
• W3129338704 type Work @default.
• W3129338704 sameAs 3129338704 @default.
• W3129338704 citedByCount "8" @default.
• W3129338704 countsByYear W31293387042022 @default.
• W3129338704 countsByYear W31293387042023 @default.
• W3129338704 crossrefType "journal-article" @default.
• W3129338704 hasAuthorship W3129338704A5049459342 @default.
• W3129338704 hasAuthorship W3129338704A5049974611 @default.
• W3129338704 hasAuthorship W3129338704A5059927670 @default.
• W3129338704 hasAuthorship W3129338704A5066754863 @default.
• W3129338704 hasBestOaLocation W31293387041 @default.
• W3129338704 hasConcept C119857082 @default.
• W3129338704 hasConcept C124101348 @default.
• W3129338704 hasConcept C139502532 @default.
• W3129338704 hasConcept C145642194 @default.
• W3129338704 hasConcept C154945302 @default.
• W3129338704 hasConcept C160735492 @default.
• W3129338704 hasConcept C162324750 @default.
• W3129338704 hasConcept C177264268 @default.
• W3129338704 hasConcept C17744445 @default.
• W3129338704 hasConcept C178790620 @default.
• W3129338704 hasConcept C185592680 @default.
• W3129338704 hasConcept C199360897 @default.
• W3129338704 hasConcept C199539241 @default.
• W3129338704 hasConcept C2776359362 @default.
• W3129338704 hasConcept C2776760102 @default.
• W3129338704 hasConcept C2779227376 @default.
• W3129338704 hasConcept C2908647359 @default.
• W3129338704 hasConcept C3019952477 @default.
• W3129338704 hasConcept C41008148 @default.
• W3129338704 hasConcept C45827449 @default.
• W3129338704 hasConcept C50522688 @default.
• W3129338704 hasConcept C54355233 @default.
• W3129338704 hasConcept C59404180 @default.
• W3129338704 hasConcept C66782513 @default.
• W3129338704 hasConcept C71924100 @default.
• W3129338704 hasConcept C86803240 @default.
• W3129338704 hasConcept C94625758 @default.
• W3129338704 hasConcept C99454951 @default.
• W3129338704 hasConceptScore W3129338704C119857082 @default.
• W3129338704 hasConceptScore W3129338704C124101348 @default.
• W3129338704 hasConceptScore W3129338704C139502532 @default.
• W3129338704 hasConceptScore W3129338704C145642194 @default.
• W3129338704 hasConceptScore W3129338704C154945302 @default.
• W3129338704 hasConceptScore W3129338704C160735492 @default.
• W3129338704 hasConceptScore W3129338704C162324750 @default.
• W3129338704 hasConceptScore W3129338704C177264268 @default.
• W3129338704 hasConceptScore W3129338704C17744445 @default.
• W3129338704 hasConceptScore W3129338704C178790620 @default.
• W3129338704 hasConceptScore W3129338704C185592680 @default.
• W3129338704 hasConceptScore W3129338704C199360897 @default.

• next