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Predictive modeling of clinical trial terminations using feature engineering and embedding learning.

Magdalyn E Elkin1, Xingquan Zhu2

  • 1Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.

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Machine learning identifies factors linked to clinical trial termination and predicts outcomes. This approach aids stakeholders in planning and minimizing costs by understanding trial success probabilities.

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Area of Science:

  • Clinical trial management
  • Data science in healthcare
  • Predictive analytics

Background:

  • Clinical trials are essential for medical advancement but face high termination rates.
  • Understanding factors contributing to trial termination is crucial for optimizing research and resource allocation.
  • Predictive models can help mitigate risks and improve the efficiency of clinical research.

Purpose of the Study:

  • To identify common factors and markers associated with terminated clinical trials.
  • To develop an accurate machine learning model for predicting clinical trial termination.
  • To provide stakeholders with insights for better trial planning and cost reduction.

Main Methods:

  • Utilized a dataset of 311,260 trials to create a testbed of 68,999 samples.
  • Engineered 640 features encompassing trial administration, eligibility, study information, and criteria.
  • Employed feature ranking, sampling, and ensemble learning techniques for analysis and prediction.

Main Results:

  • Feature ranking identified key factors related to termination, including trial eligibility and inclusion/exclusion criteria.
  • Achieved over 67% Balanced Accuracy and 0.73 AUC (Area Under the Curve) in predicting clinical trial termination.
  • Demonstrated the efficacy of machine learning in achieving satisfactory prediction results for clinical trial studies.

Conclusions:

  • Machine learning offers a viable approach to understanding and predicting clinical trial termination.
  • Identifying key predictive features can guide stakeholders in proactive trial design and management.
  • Accurate prediction of trial termination can lead to significant cost savings and improved research outcomes.