autoFlagR: AI-Driven Anomaly Detection for Data Quality
Abstract
Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) , Breunig et al. (2000) .
Fields of Study & Subjects
Anomaly Detection Techniques and Applications
Data Quality and Management
Medical Coding and Health Information
Publication details
- Publication date
- 15 January 2026
- Document type
- Dataset