Researchers from the Massachusetts Institute of Technology (MIT) Sloan and CSAIL have applied artificial intelligence (AI) techniques to predict the outcomes of randomised clinical trials.
A new study published in the Harvard Data Science Review by MIT researchers applies machine-learning and statistical techniques to predict the trial outcomes for new drugs and devices.
The latest research is part of an ongoing collaboration between the MIT Laboratory for Financial Engineering (LFE) and Informa Pharma Intelligence, known as Project Analytics for Life-sciences Professionals and Healthcare Advocates (ALPHA).
Leveraging Informa datasets from Citeline and machine learning, the project will train and validate its predictive models to offer timely and accurate estimates of the risks involved in clinical trials to the entire biopharma ecosystem.
Informa business intelligence division Corporate Development executive vice-president Mark Gordon said: “Anyone involved in the clinical trials process –– from researchers all the way down to the patient –– can benefit from greater understanding of the landscape and use of new technologies evaluating what’s working and what’s not.”
Project ALPHA aims to help patients and their families by developing analytics using which investors and biopharma professionals can manage the risks associated with the development of drugs and devices.
The recent study used a set of data for evaluating the success or failure of clinical trial outcomes.
Built on the stands of prior study, the new publication uses more than 140 features including trial outcome, trial status, trial accrual rates, prior approval for another indication, and sponsor track record to predict outcomes of clinical trials.