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Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research: Implementation, Evaluation, and

Vendula Churová1,2, Roman Vyškovský1,2, Kateřina Maršálová1

  • 1Faculty of Medicine, Masaryk University, Brno, Czech Republic.

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|April 14, 2021
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Summary
This summary is machine-generated.

This study introduces a machine learning algorithm for detecting data anomalies in clinical research. The algorithm achieves over 85% sensitivity in identifying errors, ensuring high data quality for evidence-based medicine.

Keywords:
EDC systemanomaly detectionclinical research datadata qualityreal-world evidenceregistry database

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Area of Science:

  • Clinical research informatics
  • Data science in medicine

Background:

  • Data quality is crucial for evidence-based medicine and clinical research.
  • Input data in clinical trials face risks of falsification, fabrication, and mishandling.

Purpose of the Study:

  • To describe a machine learning algorithm for detecting anomalous data patterns.
  • To address errors arising from carelessness, systematic issues, or intentional data fabrication.

Main Methods:

  • An electronic data capture (EDC) system with an integrated machine learning algorithm was developed.
  • The algorithm combines clustering with seven distance metrics to quantify anomaly strength.
  • Performance was evaluated using simulated and real-world data from neuroscience clinical registries.

Main Results:

  • The best performing distance metric combination included Canberra, Manhattan, and Mahalanobis.
  • Cosine and Chebyshev metrics showed the lowest performance as single classifiers.
  • The algorithm demonstrated robust detection capabilities across different registries.

Conclusions:

  • The machine learning algorithm is universal and can be implemented in various EDC systems.
  • The developed algorithm achieves a sensitivity exceeding 85% for anomalous data detection.
  • This contributes to enhancing data integrity in clinical research.