Article

A Comparison of Manual and Automated Approaches to Developing Computable Algorithms for Identifying Acute Pancreatitis

2026-06-08

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> Clinical phenotyping methods that rely on clinical and informatics expertise can be time-intensive and costly. We tested both manual and highly automated approaches using electronic health record (EHR) data to identify an FDA Sentinel Initiative health outcome of interest, acute pancreatitis. <h4>Materials and Methods</h4> We trained and evaluated machine learning algorithms...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 172d4c84-0ca2-501f-b8a2-3b8e9ed09511
DOI: 10.64898/2026.06.05.26354934

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Comparison of Manual and Automated Approaches to Developing Computable Algorithms for Identifying Acute PancreatitisDOI 10.64898/2026.06.05.26354934Read

Augmented Intelligence with Natural Language Processing Applied to Electronic Health Records is Useful for Identifying Patients with Non-Alcoholic Fatty Liver Disease at Risk for Disease ProgressionSemantic proximity Read

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic proximity Read

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data.Semantic proximity Read

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic proximity Read

Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.Semantic proximity Read

Rapid Identification and Phenotyping of Nonalcoholic Fatty Liver Disease Patients Using an Algorithmic Approach in Diverse, Urban Healthcare SystemsSemantic proximity Read

Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine-based approach in diverse healthcare systems.Semantic proximity Read

Trends and opportunities in computable clinical phenotyping: A scoping review.Semantic proximity Read

Application of concise machine learning to construct accurate and interpretable EHR computable phenotypesSemantic proximity Read

Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores from Colonoscopy ReportsSemantic proximity Read

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.Semantic proximity Read

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithmsSemantic proximity Read