Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379352900> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W4379352900 endingPage "302" @default.
- W4379352900 startingPage "293" @default.
- W4379352900 abstract "Early diagnosis and treatment of Crohn’s Disease (CD) is associated with decreased risk of surgery and complications. However, diagnostic delay is common in clinical practice. In order to better understand CD risk factors and disease indicators, we identified incident CD patients and controls within the Mount Sinai Data Warehouse (MSDW) and developed machine learning (ML) models for disease prediction. CD incident cases were defined based on CD diagnosis codes, medication prescriptions, healthcare utilization before first CD diagnosis, and clinical text, using structured Electronic Health Records (EHR) and clinical notes from MSDW. Cases were matched to controls based on sex, age and healthcare utilization. Thus, we identified 249 incident CD cases and 1,242 matched controls in MSDW. We excluded data from 180 days before first CD diagnosis for cohort characterization and predictive modeling. Clinical text was encoded by term frequency-inverse document frequency and structured EHR features were aggregated. We compared three ML models: Logistic Regression, Random Forest, and XGBoost. Gastrointestinal symptoms, for instance anal fistula and irritable bowel syndrome, are significantly overrepresented in cases at least 180 days before the first CD code (prevalence of 33% in cases compared to 12% in controls). XGBoost is the best performing model to predict CD with an AUROC of 0.72 based on structured EHR data only. Features with highest predictive importance from structured EHR include anemia lab values and race (white). The results suggest that ML algorithms could enable earlier diagnosis of CD and reduce the diagnostic delay." @default.
- W4379352900 created "2023-06-05" @default.
- W4379352900 creator A5006643663 @default.
- W4379352900 creator A5012477767 @default.
- W4379352900 creator A5027433317 @default.
- W4379352900 creator A5033087694 @default.
- W4379352900 creator A5038400064 @default.
- W4379352900 creator A5062892376 @default.
- W4379352900 creator A5088664462 @default.
- W4379352900 date "2023-01-01" @default.
- W4379352900 modified "2023-09-26" @default.
- W4379352900 title "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records from a Large Integrated Health System" @default.
- W4379352900 cites W1547943993 @default.
- W4379352900 cites W2119110156 @default.
- W4379352900 cites W2156665896 @default.
- W4379352900 cites W2395172628 @default.
- W4379352900 cites W2791458756 @default.
- W4379352900 cites W2911964244 @default.
- W4379352900 cites W2976146569 @default.
- W4379352900 cites W2999615587 @default.
- W4379352900 cites W3102476541 @default.
- W4379352900 cites W3214405491 @default.
- W4379352900 cites W4211138554 @default.
- W4379352900 cites W4225493996 @default.
- W4379352900 cites W4315620344 @default.
- W4379352900 cites W4320181906 @default.
- W4379352900 doi "https://doi.org/10.1007/978-3-031-34344-5_35" @default.
- W4379352900 hasPublicationYear "2023" @default.
- W4379352900 type Work @default.
- W4379352900 citedByCount "0" @default.
- W4379352900 crossrefType "book-chapter" @default.
- W4379352900 hasAuthorship W4379352900A5006643663 @default.
- W4379352900 hasAuthorship W4379352900A5012477767 @default.
- W4379352900 hasAuthorship W4379352900A5027433317 @default.
- W4379352900 hasAuthorship W4379352900A5033087694 @default.
- W4379352900 hasAuthorship W4379352900A5038400064 @default.
- W4379352900 hasAuthorship W4379352900A5062892376 @default.
- W4379352900 hasAuthorship W4379352900A5088664462 @default.
- W4379352900 hasConcept C107327155 @default.
- W4379352900 hasConcept C119857082 @default.
- W4379352900 hasConcept C126322002 @default.
- W4379352900 hasConcept C151956035 @default.
- W4379352900 hasConcept C154945302 @default.
- W4379352900 hasConcept C160735492 @default.
- W4379352900 hasConcept C162324750 @default.
- W4379352900 hasConcept C194828623 @default.
- W4379352900 hasConcept C195910791 @default.
- W4379352900 hasConcept C2778271842 @default.
- W4379352900 hasConcept C2779134260 @default.
- W4379352900 hasConcept C2779280984 @default.
- W4379352900 hasConcept C2908647359 @default.
- W4379352900 hasConcept C3018060332 @default.
- W4379352900 hasConcept C3020144179 @default.
- W4379352900 hasConcept C41008148 @default.
- W4379352900 hasConcept C45827449 @default.
- W4379352900 hasConcept C50522688 @default.
- W4379352900 hasConcept C63527458 @default.
- W4379352900 hasConcept C71924100 @default.
- W4379352900 hasConcept C72563966 @default.
- W4379352900 hasConcept C99454951 @default.
- W4379352900 hasConceptScore W4379352900C107327155 @default.
- W4379352900 hasConceptScore W4379352900C119857082 @default.
- W4379352900 hasConceptScore W4379352900C126322002 @default.
- W4379352900 hasConceptScore W4379352900C151956035 @default.
- W4379352900 hasConceptScore W4379352900C154945302 @default.
- W4379352900 hasConceptScore W4379352900C160735492 @default.
- W4379352900 hasConceptScore W4379352900C162324750 @default.
- W4379352900 hasConceptScore W4379352900C194828623 @default.
- W4379352900 hasConceptScore W4379352900C195910791 @default.
- W4379352900 hasConceptScore W4379352900C2778271842 @default.
- W4379352900 hasConceptScore W4379352900C2779134260 @default.
- W4379352900 hasConceptScore W4379352900C2779280984 @default.
- W4379352900 hasConceptScore W4379352900C2908647359 @default.
- W4379352900 hasConceptScore W4379352900C3018060332 @default.
- W4379352900 hasConceptScore W4379352900C3020144179 @default.
- W4379352900 hasConceptScore W4379352900C41008148 @default.
- W4379352900 hasConceptScore W4379352900C45827449 @default.
- W4379352900 hasConceptScore W4379352900C50522688 @default.
- W4379352900 hasConceptScore W4379352900C63527458 @default.
- W4379352900 hasConceptScore W4379352900C71924100 @default.
- W4379352900 hasConceptScore W4379352900C72563966 @default.
- W4379352900 hasConceptScore W4379352900C99454951 @default.
- W4379352900 hasLocation W43793529001 @default.
- W4379352900 hasOpenAccess W4379352900 @default.
- W4379352900 hasPrimaryLocation W43793529001 @default.
- W4379352900 hasRelatedWork W2002210145 @default.
- W4379352900 hasRelatedWork W2170162894 @default.
- W4379352900 hasRelatedWork W2409386786 @default.
- W4379352900 hasRelatedWork W2435880461 @default.
- W4379352900 hasRelatedWork W2513879842 @default.
- W4379352900 hasRelatedWork W2609332150 @default.
- W4379352900 hasRelatedWork W3173486420 @default.
- W4379352900 hasRelatedWork W4311480672 @default.
- W4379352900 hasRelatedWork W4379352900 @default.
- W4379352900 hasRelatedWork W115159143 @default.
- W4379352900 isParatext "false" @default.
- W4379352900 isRetracted "false" @default.
- W4379352900 workType "book-chapter" @default.