Blood counts, biopsies, experimental treatments and drug cocktails are all part of the experiences a cancer patient faces. By gathering this information from hundreds of thousands of people and analyzing it, scientists could learn more about the disease itself.
A team at Memorial Sloan Kettering Cancer Center in New York is training an artificial intelligence to find similarities between these cases that doctors might miss. Using the clinical notes for these patients, the software combs over 100 million sentences.
“We’re looking into the exhaust of all that data to try to find something interesting,” says Gunnar Rätsch, a data scientist at Memorial Sloan Kettering who is leading the team.
The concept developed by Rätsch and his team is simple, but has extraordinary implications. What they did is construct computational models that analyze a person’s condition, how it compares to others, and what the future course of their disease is likely to be.
“Once we have that, we can think about how to treat the patient best.”