Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records

Daren Kuwaye; Hyunkeun Cho

doi:10.1002/sim.10209

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Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records

Journal article

Open access

Peer reviewed

Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records

Daren Kuwaye and Hyunkeun Cho

Statistics in medicine, Vol.43(24), pp.4768-4777

10/30/2024

DOI: 10.1002/sim.10209

PMID: 39225196

Appears in UI Libraries Support Open Access

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https://doi.org/10.1002/sim.10209View

Published (Version of record) Open Access

Abstract

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean-shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real-world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis.

Electronic Health Records

Computer Simulation

Data Accuracy

Data Interpretation, Statistical

Humans

Models, Statistical

Reproducibility of Results

UIOWA OA Agreement

Details

Title: Subtitle: Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records
Creators: Daren Kuwaye - University of Iowa
Hyunkeun Cho - University of Iowa, Biostatistics
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.43(24), pp.4768-4777
Publisher: Wiley
DOI: 10.1002/sim.10209
PMID: 39225196
ISSN: 0277-6715
eISSN: 1097-0258
Language: English
Date published: 10/30/2024
Academic Unit: Biostatistics
Record Identifier: 9984741058602771

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