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

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.