Machine-learning model predicts remission, relapse in cancer patients

Researchers have developed an algorithm to accurately predict which patients diagnosed with acute myelogenous leukemia (AML), a cancer of the blood and bone marrow, will go into remission following treatment and which ones will relapse. Using bone marrow data and medical histories of AML patients, as well as blood data from healthy individuals, researchers were able to teach a standard 64-bit computer workstation running Windows to predict remission with 100 percent accuracy, while relapse was correctly predicted in 90 percent of relevant 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, 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," adds Dundar. Ultimately, Bartek Rajwa, research assistant professor of computational biology in the Bindley Bioscience Center at Purdue University who collaborated with Dundar, contends that the machine-learning algorithm was better at extracting knowledge from complex data than humans performing manual analysis of cytometry data.