FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3184408143> ?p ?o ?g. }

• W3184408143 abstract "Objective: Research on pharmacovigilance from social media data has focused on mining adverse drug effects (ADEs) using annotated datasets, with publications generally focusing on one of three tasks: (i) ADE classification, (ii) named entity recognition (NER) for identifying the span of an ADE mentions, and (iii) ADE mention normalization to standardized vocabularies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions to the three tasks for large-scale analysis of social media reports for different drugs. Materials and Methods: We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average `natural balance' with ADEs present in about 7% of the Tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all three tasks. Results: The system presented achieved a classification performance of F1 = 0.63, span detection performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset. Discussion: The performance of the models continue to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements. Conclusion: Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task." @default.
• W3184408143 created "2021-08-02" @default.
• W3184408143 creator A5012311258 @default.
• W3184408143 creator A5012487697 @default.
• W3184408143 creator A5019116960 @default.
• W3184408143 creator A5022014033 @default.
• W3184408143 creator A5027124439 @default.
• W3184408143 creator A5040948702 @default.
• W3184408143 creator A5068908108 @default.
• W3184408143 creator A5078878309 @default.
• W3184408143 date "2020-12-16" @default.
• W3184408143 modified "2023-10-16" @default.
• W3184408143 title "DeepADEMiner: A Deep Learning Pharmacovigilance Pipeline for Extraction and Normalization of Adverse Drug Effect Mentions on Twitter" @default.
• W3184408143 cites W1809871100 @default.
• W3184408143 cites W2071478164 @default.
• W3184408143 cites W2102742655 @default.
• W3184408143 cites W2153972688 @default.
• W3184408143 cites W2159583324 @default.
• W3184408143 cites W2171469118 @default.
• W3184408143 cites W2250539671 @default.
• W3184408143 cites W2493916176 @default.
• W3184408143 cites W2509884321 @default.
• W3184408143 cites W2793300267 @default.
• W3184408143 cites W2808345718 @default.
• W3184408143 cites W2880875857 @default.
• W3184408143 cites W2895296143 @default.
• W3184408143 cites W2907473500 @default.
• W3184408143 cites W2912695917 @default.
• W3184408143 cites W2945785264 @default.
• W3184408143 cites W2953902152 @default.
• W3184408143 cites W2963341956 @default.
• W3184408143 cites W2963626623 @default.
• W3184408143 cites W2965373594 @default.
• W3184408143 cites W2970597249 @default.
• W3184408143 cites W2972736338 @default.
• W3184408143 cites W2973038827 @default.
• W3184408143 cites W2976193469 @default.
• W3184408143 cites W3098115739 @default.
• W3184408143 cites W3116070535 @default.
• W3184408143 doi "https://doi.org/10.1101/2020.12.15.20248229" @default.
• W3184408143 hasPublicationYear "2020" @default.
• W3184408143 type Work @default.
• W3184408143 sameAs 3184408143 @default.
• W3184408143 citedByCount "3" @default.
• W3184408143 countsByYear W31844081432021 @default.
• W3184408143 countsByYear W31844081432022 @default.
• W3184408143 crossrefType "posted-content" @default.
• W3184408143 hasAuthorship W3184408143A5012311258 @default.
• W3184408143 hasAuthorship W3184408143A5012487697 @default.
• W3184408143 hasAuthorship W3184408143A5019116960 @default.
• W3184408143 hasAuthorship W3184408143A5022014033 @default.
• W3184408143 hasAuthorship W3184408143A5027124439 @default.
• W3184408143 hasAuthorship W3184408143A5040948702 @default.
• W3184408143 hasAuthorship W3184408143A5068908108 @default.
• W3184408143 hasAuthorship W3184408143A5078878309 @default.
• W3184408143 hasBestOaLocation W31844081431 @default.
• W3184408143 hasConcept C108583219 @default.
• W3184408143 hasConcept C118552586 @default.
• W3184408143 hasConcept C119857082 @default.
• W3184408143 hasConcept C123657996 @default.
• W3184408143 hasConcept C124101348 @default.
• W3184408143 hasConcept C127413603 @default.
• W3184408143 hasConcept C136764020 @default.
• W3184408143 hasConcept C136886441 @default.
• W3184408143 hasConcept C142362112 @default.
• W3184408143 hasConcept C144024400 @default.
• W3184408143 hasConcept C153349607 @default.
• W3184408143 hasConcept C154945302 @default.
• W3184408143 hasConcept C175309249 @default.
• W3184408143 hasConcept C19165224 @default.
• W3184408143 hasConcept C199360897 @default.
• W3184408143 hasConcept C201995342 @default.
• W3184408143 hasConcept C204321447 @default.
• W3184408143 hasConcept C2522767166 @default.
• W3184408143 hasConcept C2779135771 @default.
• W3184408143 hasConcept C2780035454 @default.
• W3184408143 hasConcept C2780451532 @default.
• W3184408143 hasConcept C41008148 @default.
• W3184408143 hasConcept C43521106 @default.
• W3184408143 hasConcept C518677369 @default.
• W3184408143 hasConcept C57658597 @default.
• W3184408143 hasConcept C71924100 @default.
• W3184408143 hasConcept C87717796 @default.
• W3184408143 hasConceptScore W3184408143C108583219 @default.
• W3184408143 hasConceptScore W3184408143C118552586 @default.
• W3184408143 hasConceptScore W3184408143C119857082 @default.
• W3184408143 hasConceptScore W3184408143C123657996 @default.
• W3184408143 hasConceptScore W3184408143C124101348 @default.
• W3184408143 hasConceptScore W3184408143C127413603 @default.
• W3184408143 hasConceptScore W3184408143C136764020 @default.
• W3184408143 hasConceptScore W3184408143C136886441 @default.
• W3184408143 hasConceptScore W3184408143C142362112 @default.
• W3184408143 hasConceptScore W3184408143C144024400 @default.
• W3184408143 hasConceptScore W3184408143C153349607 @default.
• W3184408143 hasConceptScore W3184408143C154945302 @default.
• W3184408143 hasConceptScore W3184408143C175309249 @default.
• W3184408143 hasConceptScore W3184408143C19165224 @default.
• W3184408143 hasConceptScore W3184408143C199360897 @default.
• W3184408143 hasConceptScore W3184408143C201995342 @default.
• W3184408143 hasConceptScore W3184408143C204321447 @default.

• next