Biodesix’s proprietary machine learning platform builds on recent advances in the artificial intelligence field to address the unique needs of clinical diagnostics.
The practice of medicine revolves around information-based decision making. In the fields of cancer biomarker and drug development research, this information is becoming increasingly rich with the advent of new technologies. Scientists’ ability to make sense of this wealth of healthcare data and distill it down to simple hypotheses to inform clinical decisions will inevitably become more and more difficult.
Artificial intelligence is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages1. Machine learning is a subset of artificial intelligence, where the machine has the ability to acquire its own knowledge by extracting patterns from data2. The application of machine learning in healthcare has the potential to uncover clinically-relevant patterns and meaning in our healthcare data; helping us better understand complex diseases like cancer.
1Oxford Dictionary, “Artificial Intelligence”, 2018. 2Goodfellow et al 2016 – Deep Learning.
At Biodesix, we use the Diagnostic Cortex® platform, which is based on modern machine learning techniques to design tests that are reproducible, robust, and answer critical clinical questions for our partners. Based on data found in the circulating proteome and genome, we design tests that support treatment decisions including patient selection for immunotherapies, novel therapy combinations, or alternative treatment pathways.
“The accurate prediction of a disease outcome is one of the most interesting and challenging tasks for physicians. As a result, machine learning methods have become a popular tool for medical researchers. These techniques can discover and identify patterns and relationships between them, from complex datasets, while they are able to effectively predict future outcomes of a cancer type.”
– KOUROU, K. “MACHINE LEARNING APPLICATIONS IN CANCER PROGNOSIS AND PREDICTION”. CSBJ, 2015.