In:
Clinical and Translational Gastroenterology, Ovid Technologies (Wolters Kluwer Health), ( 2023-09-12)
Abstract:
Screening for Barrett’s esophagus (BE) is suggested in those with risk factors but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools, integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver operating curve [AUROC] ≤ 0.7) and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. Methods: The clinical data analytics platform (CDAP), a de-identified EHR database of 6 million Mayo Clinic patients, was utilized to predict BE and EAC risk. BE and EAC cases and controls were identified using ICD codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based machine-learning (ML) model architecture was used to develop predictive models. Results: We identified 8476 BE cases, 1539 EAC cases and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76% and 0.84 respectively. The EAC ML transformer model had an overall sensitivity, specificity and AUROC of 84%, 70% and 0.84 respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors such as coronary artery disease, serum triglycerides and electrolytes. Discussion: ML models developed on a EHR database can predict incident BE and EAC risk with improved accuracy, compared to conventional risk factor-based risk scores. Such a model may enable effective implementation of minimally invasive screening technology.
Type of Medium:
Online Resource
ISSN:
2155-384X
DOI:
10.14309/ctg.0000000000000637
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2023
detail.hit.zdb_id:
2581516-7
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