Matches in SemOpenAlex for { <https://semopenalex.org/work/W2890937487> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W2890937487 abstract "Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes pertaining to diabetes and validate the algorithms at two institutions. The data used are 2,000 EHR progress notes retrieved from patients with diabetes and all notes were annotated manually as diabetic or non-diabetic. Several deep learning classifiers were developed, and their performances were evaluated with the area under the ROC curve (AUC). The convolutional neural network (CNN) model with a separable convolution layer accurately identified diabetes-related notes in the Brigham and Womens Hospital testing set with the highest AUC of 0.975. Deep learning classifiers can be used to identify EHR progress notes pertaining to diabetes. In particular, the CNN-based classifier can achieve a higher AUC than an SVM-based classifier." @default.
- W2890937487 created "2018-09-27" @default.
- W2890937487 creator A5004738173 @default.
- W2890937487 creator A5068040826 @default.
- W2890937487 date "2018-09-16" @default.
- W2890937487 modified "2023-09-27" @default.
- W2890937487 title "Development of deep learning algorithms to categorize free-text notes pertaining to diabetes: convolution neural networks achieve higher accuracy than support vector machines" @default.
- W2890937487 cites W2024968541 @default.
- W2890937487 cites W2120615054 @default.
- W2890937487 cites W2154022005 @default.
- W2890937487 cites W2180769951 @default.
- W2890937487 cites W2186845332 @default.
- W2890937487 cites W2251143283 @default.
- W2890937487 cites W2252215182 @default.
- W2890937487 cites W2284289336 @default.
- W2890937487 cites W2297405797 @default.
- W2890937487 cites W2407776548 @default.
- W2890937487 cites W2409650203 @default.
- W2890937487 cites W2415204069 @default.
- W2890937487 cites W2557738935 @default.
- W2890937487 cites W2784570262 @default.
- W2890937487 cites W2949541494 @default.
- W2890937487 cites W2950141408 @default.
- W2890937487 cites W2950967261 @default.
- W2890937487 hasPublicationYear "2018" @default.
- W2890937487 type Work @default.
- W2890937487 sameAs 2890937487 @default.
- W2890937487 citedByCount "0" @default.
- W2890937487 crossrefType "posted-content" @default.
- W2890937487 hasAuthorship W2890937487A5004738173 @default.
- W2890937487 hasAuthorship W2890937487A5068040826 @default.
- W2890937487 hasConcept C108583219 @default.
- W2890937487 hasConcept C11413529 @default.
- W2890937487 hasConcept C119857082 @default.
- W2890937487 hasConcept C12267149 @default.
- W2890937487 hasConcept C154945302 @default.
- W2890937487 hasConcept C41008148 @default.
- W2890937487 hasConcept C50644808 @default.
- W2890937487 hasConcept C81363708 @default.
- W2890937487 hasConcept C94124525 @default.
- W2890937487 hasConcept C95623464 @default.
- W2890937487 hasConceptScore W2890937487C108583219 @default.
- W2890937487 hasConceptScore W2890937487C11413529 @default.
- W2890937487 hasConceptScore W2890937487C119857082 @default.
- W2890937487 hasConceptScore W2890937487C12267149 @default.
- W2890937487 hasConceptScore W2890937487C154945302 @default.
- W2890937487 hasConceptScore W2890937487C41008148 @default.
- W2890937487 hasConceptScore W2890937487C50644808 @default.
- W2890937487 hasConceptScore W2890937487C81363708 @default.
- W2890937487 hasConceptScore W2890937487C94124525 @default.
- W2890937487 hasConceptScore W2890937487C95623464 @default.
- W2890937487 hasLocation W28909374871 @default.
- W2890937487 hasOpenAccess W2890937487 @default.
- W2890937487 hasPrimaryLocation W28909374871 @default.
- W2890937487 hasRelatedWork W1170932175 @default.
- W2890937487 hasRelatedWork W1933242123 @default.
- W2890937487 hasRelatedWork W2052525903 @default.
- W2890937487 hasRelatedWork W2367952659 @default.
- W2890937487 hasRelatedWork W2767350626 @default.
- W2890937487 hasRelatedWork W2786899599 @default.
- W2890937487 hasRelatedWork W2921671163 @default.
- W2890937487 hasRelatedWork W2962122314 @default.
- W2890937487 hasRelatedWork W2969705914 @default.
- W2890937487 hasRelatedWork W2971515142 @default.
- W2890937487 hasRelatedWork W2982370662 @default.
- W2890937487 hasRelatedWork W2990291356 @default.
- W2890937487 hasRelatedWork W3007318604 @default.
- W2890937487 hasRelatedWork W3021382697 @default.
- W2890937487 hasRelatedWork W3021667312 @default.
- W2890937487 hasRelatedWork W3041169526 @default.
- W2890937487 hasRelatedWork W3099007808 @default.
- W2890937487 hasRelatedWork W3170318683 @default.
- W2890937487 hasRelatedWork W3192370123 @default.
- W2890937487 hasRelatedWork W3194006990 @default.
- W2890937487 isParatext "false" @default.
- W2890937487 isRetracted "false" @default.
- W2890937487 magId "2890937487" @default.
- W2890937487 workType "article" @default.