Binghamton University Research News
  • News
  • Features
  • Faculty
  • Students
  • Videos
  • Photos
  • Subscribe

Machine learning may improve medicine

By Barb Van Atta • Jul 23, 2012 • Students•   

A host of novelists and movie directors have put a kink in the way bioengineers such as Daniel E. Margolis can describe their work to laymen. Co-opted by the science fiction community, the term “artificial intelligence” now carries too much fantastical baggage. Now, “most work in A.I. is called ‘machine learning,’” says Margolis, a doctoral student at Binghamton.

Margolis’ machine learning is in the area of “clinical support systems,” which are computer programs that can learn to make decisions such as diagnosis, prognosis and treatment of medical conditions. “There are various names for these types of computer programs … such as machine learning, data mining or pattern recognition,” says Margolis, who has collaborated with researchers at the H. Lee Moffitt Cancer Center and Research Institute in Tampa, Fla., and at the University of Arizona.

“The work with Moffitt dealt with a newly developed technique that accomplished two difficult tasks: combining the decisions from multiple computer programs intelligently and combining the data from multiple clinical tests intelligently,” Margolis explains. “For example, assume a doctor has several computer programs that try to automatically diagnose lung cancer from a DNA microarray or CT scans. However, each program only works well with certain patients and/or a certain test. This new technique would learn which combination of programs and tests works best for a particular patient.”

The research with Arizona used machine learning to improve methods of evaluating cancer treatment response. “We took the data that is normally used when looking at CT scans, added additional clinical data and then sent it through a machine learning method,” Margolis says. “We showed that machine learning methods could be designed to allow any new type of data to be added, and these methods performed far better than the current ‘gold standard’ method.”

He says they also noticed interesting patterns in “observer variability,” that is, the fact that doctors disagree with each other — and sometimes with themselves. These patterns showed that machine learning methods eventually could be developed to objectively score and train doctors in fields such as radiology.

Margolis says an increase in the use of machines would not affect a patient’s opportunity to receive personalized care. “The goal is for cheaper, better detection methods using computers,” he says. “Nobody would not get the medical test they needed.”

Once he completes his doctorate in systems science, he hopes to work in industrial research. His advisor, Walker Land, predicts success.

“Dan is best described as a unique thinker, one who sees a difficulty as an opportunity rather than a problem,” says Land, research professor of bioengineering. “It’s just the kind of thinking required for successful bioinformatics/biomedical research.”

Like this article? Please share!
bioengineeringhealthhealthcare
User interface design drives marketing research
Future lawyer explores patients’ rights issues

You Might Also Like

  • Microelectronics industry has its eye on grad student’s research

  • Student pursues research and creative writing

  • Future doctor finds passion for research

  • Student focuses on the ethics of revolution

    Research in the news

    • Modern medicine traces its scientific roots to the Middle Ages

    • Are people born with good balance?

    • Earth to be hit by ‘widespread pest outbreaks’ — and it’s our fault

    • For EV batteries, lithium iron phosphate narrows the gap with nickel, cobalt

    • The revolt of the other mothers

    Recent Comments

    • Resume Format on Computer program spots narcissistic execs
    • Ann Walker on Wasps may provide climate change insights
    • Dejen Habtom on Ancient seawater may yield climate change insights
    • Don Franck on Binghamton battery project wins $500,000; will compete for $100M
    • Dave on Anechoic chamber puts sound to the test
    Binghamton University Binghamton University

    © 2025 Binghamton University State University of New York
    Images used throughout this site are copyright protected. For permission and terms of use, visit the about us page