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Detecting Irregular Data in Clinical Trials with Machine Learning

Published on:

9 November 2023

Primary Category:

Machine Learning

Paper Authors:

Walter Nelson,

Jonathan Ranisau,

Jeremy Petch


Key Details

Tested outlier detection algorithms on 800+ datasets from 7 major clinical trials

Algorithms often succeeded at finding irregular data without supervision

But no single algorithm performed best across all datasets

Proposed ensemble method combining multiple algorithms improved performance

Provides guidance on using ML for data quality in clinical trials

AI generated summary

Detecting Irregular Data in Clinical Trials with Machine Learning

This paper evaluates machine learning algorithms for detecting irregular data in large clinical trials. The algorithms were tested on over 800 datasets from 7 major trials with 77,000 patients. The algorithms often succeeded in finding irregular data without supervision. However, performance varied across datasets, so no single algorithm worked best. The paper proposes an ensemble method to combine multiple algorithms, which improved performance. Overall, the study provides guidance on applying machine learning for data quality in clinical trials.

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