In 2016, IBM Watson Health and the Broad Institute have announced a five year initiative to promote using machine learning and genomics to better understand why and how cancers become resistant to therapies.

Over an initial span of three years, IBM and the Broad Institute will work on building algorithms to identify specific health conditions like cardiac arrest and atrial fibrillation.

The project will help provide doctors with tools to tap into the potential of genomics data, and better understand the intrinsic possibility an individual has for a certain disease.

The initiative will have biobank data, genomic information, and electronic health records based on population and hospital to build and expand the predictive power of polygenic scoring or genetic risk scoring.

IBM Watson Health senior vice president John Kelly said: “We’re working directly with the physician-scientists at the Broad Institute to evolve how AI can help unlock undiscovered clues about human health. We’ve built a deep expertise in applying AI to understand the complexities and meaning of immense amounts of data, such as genomics and health records.

Our latest collaboration will combine these capabilities with clinical insights, and refine how technology can provide explainable and valuable insights to clinicians as they study and treat serious conditions such as cardiovascular disease.”

IBM and the Broad Institute are targeting to build algorithms that can pinpoint and learn from trends in these data points, and then indicate a potential predisposition to certain health conditions.

The project is also planned to make insights and tools including methods to calculate an individual’s risk of developing common diseases based on millions of variants in the genome, broadly available to the research community.

For accurate prediction of the complex and often fatal conditions in patients, like heart attacks, sudden cardiac death, and atrial fibrillation, the AI technology aims to produce models bring together and analyze a multitude of genetic risk factors within an individual’s genome, along with existing health records and biomarkers.

Massachusetts General Hospital (MGH) center for genomic medicine director and Broad Institute cardiovascular disease initiative institute member and director Sekar Kathiresan said: “We’re excited to build upon the advances we’ve made in polygenic risk scoring utilizing vast amount of genomic data.

By coupling clinical data with genomic data, there is an exceptional opportunity to make polygenic risk scoring more robust and powerful, and ultimately transformative for patient care. Such transformation could never happen without these kinds of partnerships.”