FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4286567174> ?p ?o ?g. }

• W4286567174 endingPage "104149" @default.
• W4286567174 startingPage "104149" @default.
• W4286567174 abstract "One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates bloated notes containing large amounts of textual redundancy. Despite the rising interest in applying machine learning models to learn from real-patient data, it is unclear how the phenomenon of note bloat might affect the Natural Language Processing (NLP) models derived from these notes. Therefore, in this work we examine the impact of redundancy on deep learning-based NLP models, considering four clinical prediction tasks using a publicly available EHR database. We applied two deduplication methods to the hospital notes, identifying large quantities of redundancy, and found that removing the redundancy usually has little negative impact on downstream performances, and can in certain circumstances assist models to achieve significantly better results. We also showed it is possible to attack model predictions by simply adding note duplicates, causing changes of correct predictions made by trained models into wrong predictions. In conclusion, we demonstrated that EHR text redundancy substantively affects NLP models for clinical prediction tasks, showing that the awareness of clinical contexts and robust modeling methods are important to create effective and reliable NLP systems in healthcare contexts." @default.
• W4286567174 created "2022-07-22" @default.
• W4286567174 creator A5012453035 @default.
• W4286567174 creator A5023213398 @default.
• W4286567174 creator A5067214173 @default.
• W4286567174 creator A5074297656 @default.
• W4286567174 date "2022-09-01" @default.
• W4286567174 modified "2023-10-13" @default.
• W4286567174 title "“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks" @default.
• W4286567174 cites W1672757658 @default.
• W4286567174 cites W1919152067 @default.
• W4286567174 cites W1966132539 @default.
• W4286567174 cites W2002514548 @default.
• W4286567174 cites W2004910511 @default.
• W4286567174 cites W2014202038 @default.
• W4286567174 cites W2030498706 @default.
• W4286567174 cites W2032503600 @default.
• W4286567174 cites W2033609349 @default.
• W4286567174 cites W2082302018 @default.
• W4286567174 cites W2127397103 @default.
• W4286567174 cites W2138328644 @default.
• W4286567174 cites W2396881363 @default.
• W4286567174 cites W2434099711 @default.
• W4286567174 cites W2616920365 @default.
• W4286567174 cites W2800224756 @default.
• W4286567174 cites W2803290558 @default.
• W4286567174 cites W2808129629 @default.
• W4286567174 cites W2908201961 @default.
• W4286567174 cites W2924551358 @default.
• W4286567174 cites W2930139824 @default.
• W4286567174 cites W2934399013 @default.
• W4286567174 cites W2940854303 @default.
• W4286567174 cites W2944400536 @default.
• W4286567174 cites W2949155040 @default.
• W4286567174 cites W2952775379 @default.
• W4286567174 cites W2955483668 @default.
• W4286567174 cites W2963716420 @default.
• W4286567174 cites W2964010366 @default.
• W4286567174 cites W2964142373 @default.
• W4286567174 cites W2969881216 @default.
• W4286567174 cites W2970726176 @default.
• W4286567174 cites W2970986790 @default.
• W4286567174 cites W2975249097 @default.
• W4286567174 cites W2982054702 @default.
• W4286567174 cites W2982597005 @default.
• W4286567174 cites W2985621057 @default.
• W4286567174 cites W2993873509 @default.
• W4286567174 cites W3000686922 @default.
• W4286567174 cites W3007211628 @default.
• W4286567174 cites W3035294872 @default.
• W4286567174 cites W3035990700 @default.
• W4286567174 cites W3040715193 @default.
• W4286567174 cites W3080098168 @default.
• W4286567174 cites W3092301826 @default.
• W4286567174 cites W3094834348 @default.
• W4286567174 cites W3095791468 @default.
• W4286567174 cites W3098601872 @default.
• W4286567174 cites W3103694015 @default.
• W4286567174 cites W3105800762 @default.
• W4286567174 cites W3118485687 @default.
• W4286567174 cites W3119358510 @default.
• W4286567174 cites W3134867904 @default.
• W4286567174 cites W3168830259 @default.
• W4286567174 cites W3173251529 @default.
• W4286567174 cites W3173447414 @default.
• W4286567174 cites W3174700141 @default.
• W4286567174 cites W3177617320 @default.
• W4286567174 cites W3177765786 @default.
• W4286567174 cites W3186100284 @default.
• W4286567174 cites W3186833834 @default.
• W4286567174 cites W3200918848 @default.
• W4286567174 cites W3209293071 @default.
• W4286567174 cites W4205464244 @default.
• W4286567174 cites W4205758343 @default.
• W4286567174 cites W4288083420 @default.
• W4286567174 doi "https://doi.org/10.1016/j.jbi.2022.104149" @default.
• W4286567174 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35878821" @default.
• W4286567174 hasPublicationYear "2022" @default.
• W4286567174 type Work @default.
• W4286567174 citedByCount "8" @default.
• W4286567174 countsByYear W42865671742022 @default.
• W4286567174 countsByYear W42865671742023 @default.
• W4286567174 crossrefType "journal-article" @default.
• W4286567174 hasAuthorship W4286567174A5012453035 @default.
• W4286567174 hasAuthorship W4286567174A5023213398 @default.
• W4286567174 hasAuthorship W4286567174A5067214173 @default.
• W4286567174 hasAuthorship W4286567174A5074297656 @default.
• W4286567174 hasBestOaLocation W42865671741 @default.
• W4286567174 hasConcept C108583219 @default.
• W4286567174 hasConcept C111919701 @default.
• W4286567174 hasConcept C119857082 @default.
• W4286567174 hasConcept C152124472 @default.
• W4286567174 hasConcept C154945302 @default.
• W4286567174 hasConcept C160735492 @default.
• W4286567174 hasConcept C162324750 @default.
• W4286567174 hasConcept C204321447 @default.
• W4286567174 hasConcept C3019952477 @default.
• W4286567174 hasConcept C3020144179 @default.

• next