This platform call CHAP interrogates the deployment pipeline for a user-specified service. It then launches experiment and control clusters of that service, and routes a small amount of traffic to each. A specified FIT scenario is applied to the experimental group, and the results of the experiment are reported to the service owner.
Friday, 10 August 2018
Artificial intelligence model “learns” from patient data to make cancer treatment less toxic
Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors.
Rob Matheson | MIT News Office
August 9, 2018
August 9, 2018
MIT researchers are employing novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer.
Glioblastoma is a malignant tumor that appears in the brain or spinal cord, and prognosis for adults is no more than five years. Patients must endure a combination of radiation therapy and multiple drugs taken every month. Medical professionals generally administer maximum safe drug doses to shrink the tumor as much as possible. But these strong pharmaceuticals still cause debilitating side effects in patients.
In a paper being presented next week at the 2018 Machine Learning for Healthcare conference at Stanford University, MIT Media Lab researchers detail a model that could make dosing regimens less toxic but still effective. Powered by a “self-learning” machine-learning technique, the model looks at treatment regimens currently in use, and iteratively adjusts the doses. Eventually, it finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumor sizes to a degree comparable to that of traditional regimens.
In simulated trials of 50 patients, the machine-learning model designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumor-shrinking potential. Many times, it skipped doses altogether, scheduling administrations only twice a year instead of monthly.
“We kept the goal, where we have to help patients by reducing tumor sizes but, at the same time, we want to make sure the quality of life — the dosing toxicity — doesn’t lead to overwhelming sickness and harmful side effects,” says Pratik Shah, a principal investigator at the Media Lab who supervised this research.
The paper’s first author is Media Lab researcher Gregory Yauney.
Subscribe to:
Posts (Atom)