Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jan 14, 2021
Date Accepted: Apr 12, 2021
Date Submitted to PubMed: Apr 14, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research
ABSTRACT
Background:
Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also of being mishandled by investigators.
Objective:
The urgent need to assure the highest data quality possible has led to implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field.
Methods:
An automatic anomaly detection algorithm based on machine learning that combines clustering with a series of distance metrics is presented.
Results:
The algorithm is built in a particular electronic data capture (EDC) system that stores real-world data in clinical registries. These data, together with newly generated, simulated anomalous data were utilized to evaluate the detection performance of this algorithm.
Conclusions:
The experimental results demonstrate that the algorithm, which is universal, and as such may be implemented in other EDC systems, is capable of anomalous data detection with sensitivity exceeding 85%.
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Copyright
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