Machine Learning for Predicting Clinical Trial Outcomes

Abstract

Clinical trials, which are essential for assessing the safety and effective functioning of medical treatments, are feasible a good number of times for low costs, short timelines, and unpredicted outcomes. This study investigates the possibility of predicting clinical trial results with the use of machine learning (ML) through the use of historical trial data as well as patient demographics and biomedical markers. The use of advanced ML algorithms including supervised and ensemble learning models helps this research in identifying key factors which are likely to lead to trial success and developing predictive models in order to augment decision-making in trial design and patient recruitment. The proposed approach adopts the steps of feature selection followed by model optimization such that it achieves maximum accuracy and generalizability. The results indicate the effectiveness of the ML in the reduction of trial discrepancies providing essential information to pharmaceutical companies, clinicians, and regulatory authorities alike. The objective of this paper is to contribute to the expanding canon of literature on the applications of artificial intelligence in healthcare with a specific emphasis on its transformational potential for clinical trial methods in terms of higher efficiency and lower costs.

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