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PRACTICAL APPLICATIONS FOR MACHINE LEARNING IN CLINICAL TRIALS

How does machine learning technology stack up against a manual TMF documentation process?

DOCUMENT INDEXING

We categorized essential clinical trial documents in the TMF by reviewing each document’s TMF number, level, zone, section, artifact, sub-artifact, site, contacts, and additional key details with machine learning technologies and near-duplicate detection (NDD), an algorithmic and statistical approach to determining document resemblance.

 

CHALLENGE

Automatically classify:

  • 125,000 documents
  • 5 countries
  • 7 languages

 

RESULTS

  • No prior model training required
  • Language independent

 

* 94% MATCHING ACCURACY *

Download Practical Applications for Machine Learning in Clinical Trials

METADATA EXTRACTION

We used natural language processing and extraction tools to identify structured and fielded data in TMF documents, such as the site, country, contact, and document date.

 

CHALLENGE

Automatically extract data from:

  • 424 TMF classifications
  • 225+ eTMFs
  • 5 countries
  • 7 languages
  • 234,000 documents

 

RESULTS

  • No prior model training required
  • Language independent

 

* 98% EXTRACTION ACCURACY *