Computer predicts leukemia remission with 90% accuracy

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Researchers have developed a computer learning algorithm capable of predicting remission rates for patient with acute myelogenous leukemia (AML).

Using bone marrow data and medical histories of patients with AML, the computer was trained to predict remission with 100 percent accuracy via predictive analytics. Incidents of relapse was predicted in 90 percent of cases.

"It's pretty straightforward to teach a computer to recognize AML, once you develop a robust algorithm, and in previous work we did it with almost 100 percent accuracy," said Murat Dundar, senior author of the disease-progression study and associate professor of computer science in the School of Science at Indiana University-Purdue University Indianapolis. "What was challenging was to go beyond that work and teach the computer to accurately predict the direction of change in disease progression in AML patients, interpreting new data to predict the unknown: which new AML patients will go into remission and which will relapse."

This high rate in accurate predictions give both patient and physician the chance to prepare for future treatments. The early start in knowing what works well for each individual patient means for higher quality care and creates clinical decision support.

"Machine learning is not about modeling data. It's about extracting knowledge from the data you have so you can build a powerful, intuitive tool that can make predictions about future data that the computer has not previously seen—the machine is learning, not memorizing—and that's what we did," said Dundar, an internationally respected machine-learning scientist who specializes in teaching computers to understand biomedical data.