Clinical Trials
Lower costs, new approaches
Description
Machine learning technologies can help predict outcomes of clinical trials, leading to faster drug approval times, lower costs, and more funding to develop new treatments. Machine learning is capable of increasing clinical trial efficiency by taking in huge loads of data such as trial status, accrual rates, duration, and sponsor track record, to predict clinical trial outcomes at speeds above human capability.
While the healthcare industry is taking baby steps into using machine learning in clinical trials and for other data-generating objectives, it’s value will depend on the transparency of algorithms, robustness of data sources, and extrapolation to real-world outcomes.
Our team has experience in preclinical and clinical trials analysis. We have performed anomaly detection in clinical groups, and built robust & efficient prediction models for the drug trials in Europe.
