PharmaVOICE Blog Post

Machine Learning and the Future of Patient Forecasting in Clinical Trials

Posted By: Dan Limbach
May 6, 2019

In a June 6 webinar, industry experts from Inteliquet will demonstrate specifically how machine learning can augment and assist human intelligence to perform better patient forecasting.  By extracting pertinent EMR information, including physicians’ notes, reading binary data from images and medical scans and comparing them to a study’s inclusion and exclusion criteria, machine learning can more efficiently and effectively identify the appropriate sites and patients for clinical trial enrollment—ultimately supporting more effective trials.

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 Key Takeaways:

  • Identify various areas of clinical data and analytics where machine learning can influence clinical trials
  • Patient data can be enhanced beyond the discrete fields in an EMR system to digitally screen patients for clinical trials
  • Synthetic cohorts are an emerging way to utilize real-world data to reduce patient burden and the costs associated with running a clinical trial
  • Forecasting when patient groups will become available offers better prediction at the time of trial design as to the duration, and therefore cost, of the clinical trial
  • Finding the right patient at the right time is key to trial recruitment, and longitudinal analysis highlights the need to have ongoing monitoring of potentially eligible patients

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Who Should Attend:

  • CROs and Life Sciences, especially clinical development planning and medical affairs
  • Investigator-initiated Trial Leadership
  • Clinical Research Professionals

About the Blog Poster: Dan Limbach

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