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

ABSTRACT: In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using featu

SUBMITTER: Elkin ME 

PROVIDER: S-EPMC7876037 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature