Machine learning is being quickly adapted across the healthcare space to develop precision medicine, and it can also be leveraged to improve the development of new drug treatments and devices by improving the randomized clinical trial process, according to MIT researchers. The researchers needed to enhance data on clinical trial outcomes to better predict if drugs were likely to be approved, using machine learning and statistical techniques.
Limiting the risks of clinical trials can allow resources to be used more efficiently, with fewer failures, faster drug approval times, lower cost of capital and more funding for developing other new therapies.
They used the largest set of data to date from two proprietary pharma pipeline databases. The findings were published in the debut issue of the Harvard Data Science Review . Limiting the risks of clinical trials can allow resources to be used more efficiently, with fewer failures, faster drug approval times, lower cost of capital and more funding for developing other new therapies. “Everyone is affected by the risk of a drug failing in its clinical trial process,” lead study author Andrew Lo, director of the MIT Laboratory for Financial Engineering, said in a statement. “With more accurate measures of the risk of drug and device development, we hope to encourage greater investment at this unique inflection point in biomedicine.” Beyond offering guidance to investors, scientists, clinicians and biopharma professionals on the […]
