Clinical trials are the backbone of modern medicine, responsible for bringing innovative, safe, and efficient treatments to market after rigorous testing and evaluation. However, the conventional process of conducting these trials has often been long, expensive, and fraught with inefficiencies¹. The advent of predictive analytics is reshaping the landscape of clinical trials, ushering in a new era of precision efficiency². This blog explores how predictive analytics is transforming clinical trials, from optimizing design and recruitment to predicting outcomes and reducing risks.
multi-omics
Article Spotlight: Machine learning-powered prediction of kidney cancer recurrence
Discover how we collaborated with the UroCCR network to develop a machine learning model that outperformed the predictive performance of most usual kidney cancer risk scores.
Technical Note: Navigating overfitting during machine learning model development
We’re often asked how we avoid overfitting when developing predictive machine learning models for clinical research. This technical note explains how.
The power of multimodal data-driven medicine
Capturing the complexity of human health and disease through machine learning analysis of multimodal data has the potential to drive the future of healthcare.
Demystifying machine learning in healthcare: a layman’s guide to understanding the technology
This guide deciphers the jargon associated with machine learning in healthcare and explains why artificial intelligence is invaluable to revolutionize the capabilities of HCPs in improving patient care.
Radiomics: 5 Things You Need to Know
Radiomics maximizes upon information that’s already being collected. The difference is how that info is used.