FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386435258> ?p ?o ?g. }

• W4386435258 endingPage "104777" @default.
• W4386435258 startingPage "104777" @default.
• W4386435258 abstract "The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231." @default.
• W4386435258 created "2023-09-05" @default.
• W4386435258 creator A5006315520 @default.
• W4386435258 creator A5006785883 @default.
• W4386435258 creator A5016180789 @default.
• W4386435258 creator A5016511878 @default.
• W4386435258 creator A5033225381 @default.
• W4386435258 creator A5039207813 @default.
• W4386435258 creator A5060390916 @default.
• W4386435258 creator A5070550028 @default.
• W4386435258 creator A5071444502 @default.
• W4386435258 creator A5073142937 @default.
• W4386435258 creator A5073533486 @default.
• W4386435258 creator A5085983888 @default.
• W4386435258 creator A5089496823 @default.
• W4386435258 creator A5091653626 @default.
• W4386435258 date "2023-10-01" @default.
• W4386435258 modified "2023-10-15" @default.
• W4386435258 title "Predictive models of long COVID" @default.
• W4386435258 cites W2000445173 @default.
• W4386435258 cites W2999615587 @default.
• W4386435258 cites W3012622856 @default.
• W4386435258 cites W3015035592 @default.
• W4386435258 cites W3018023298 @default.
• W4386435258 cites W3026600546 @default.
• W4386435258 cites W3027630905 @default.
• W4386435258 cites W3043870823 @default.
• W4386435258 cites W3049452588 @default.
• W4386435258 cites W3092635280 @default.
• W4386435258 cites W3103969873 @default.
• W4386435258 cites W3109172892 @default.
• W4386435258 cites W3135108774 @default.
• W4386435258 cites W3136807555 @default.
• W4386435258 cites W3136955306 @default.
• W4386435258 cites W3153493739 @default.
• W4386435258 cites W3153912282 @default.
• W4386435258 cites W3165150342 @default.
• W4386435258 cites W3177256923 @default.
• W4386435258 cites W3179372724 @default.
• W4386435258 cites W3206064057 @default.
• W4386435258 cites W3210468723 @default.
• W4386435258 cites W3217743982 @default.
• W4386435258 cites W4206792061 @default.
• W4386435258 cites W4206896921 @default.
• W4386435258 cites W4206956005 @default.
• W4386435258 cites W4224313552 @default.
• W4386435258 cites W4280531656 @default.
• W4386435258 cites W4287510022 @default.
• W4386435258 cites W4292360315 @default.
• W4386435258 cites W4296307209 @default.
• W4386435258 cites W4302306860 @default.
• W4386435258 cites W4311134061 @default.
• W4386435258 cites W4312061512 @default.
• W4386435258 cites W4319347796 @default.
• W4386435258 cites W4320881351 @default.
• W4386435258 cites W4360600770 @default.
• W4386435258 cites W4362601943 @default.
• W4386435258 cites W4366977633 @default.
• W4386435258 cites W4377138940 @default.
• W4386435258 doi "https://doi.org/10.1016/j.ebiom.2023.104777" @default.
• W4386435258 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37672869" @default.
• W4386435258 hasPublicationYear "2023" @default.
• W4386435258 type Work @default.
• W4386435258 citedByCount "0" @default.
• W4386435258 crossrefType "journal-article" @default.
• W4386435258 hasAuthorship W4386435258A5006315520 @default.
• W4386435258 hasAuthorship W4386435258A5006785883 @default.
• W4386435258 hasAuthorship W4386435258A5016180789 @default.
• W4386435258 hasAuthorship W4386435258A5016511878 @default.
• W4386435258 hasAuthorship W4386435258A5033225381 @default.
• W4386435258 hasAuthorship W4386435258A5039207813 @default.
• W4386435258 hasAuthorship W4386435258A5060390916 @default.
• W4386435258 hasAuthorship W4386435258A5070550028 @default.
• W4386435258 hasAuthorship W4386435258A5071444502 @default.
• W4386435258 hasAuthorship W4386435258A5073142937 @default.
• W4386435258 hasAuthorship W4386435258A5073533486 @default.
• W4386435258 hasAuthorship W4386435258A5085983888 @default.
• W4386435258 hasAuthorship W4386435258A5089496823 @default.
• W4386435258 hasAuthorship W4386435258A5091653626 @default.
• W4386435258 hasConcept C105795698 @default.
• W4386435258 hasConcept C120665830 @default.
• W4386435258 hasConcept C121332964 @default.
• W4386435258 hasConcept C126322002 @default.
• W4386435258 hasConcept C151956035 @default.
• W4386435258 hasConcept C154945302 @default.
• W4386435258 hasConcept C169258074 @default.
• W4386435258 hasConcept C27158222 @default.
• W4386435258 hasConcept C2779134260 @default.
• W4386435258 hasConcept C2908647359 @default.
• W4386435258 hasConcept C3008058167 @default.
• W4386435258 hasConcept C33923547 @default.
• W4386435258 hasConcept C41008148 @default.
• W4386435258 hasConcept C45827449 @default.
• W4386435258 hasConcept C524204448 @default.
• W4386435258 hasConcept C61511704 @default.
• W4386435258 hasConcept C71924100 @default.
• W4386435258 hasConcept C72563966 @default.
• W4386435258 hasConcept C99454951 @default.

• next