MIT Sloan Health Systems Initiative
Research spotlight: Using Data Analytics for Better Healthcare Delivery
Unlocking an analytics-fueled approach to chemotherapy combinations
Researcher: Dimitris Bertsimas
Dimitris Bertsimas and his co-researchers are pioneering new ways to apply state-of-the-art analytics and machine learning to the task of designing safe and effective chemotherapy regimens. They are focusing their efforts on gastric cancer, a cancer type for which there is currently no best-in-class chemotherapy treatment regimen. Having constructed a database of 414 clinical trials for advanced gastric cancer, Bertsimas and his colleagues have now trained statistical models with randomized and non-randomized clinical data to more accurately predict survival and toxicity outcomes from combinatory chemotherapy treatments—and to evaluate those predictions.
Equipped with these first-of-their-kind methodologies that can help predict outcomes (including identifying 10–20 percent of the trials with high toxicity or efficacy issues as well as high-promise clinical trials before they are run), providers will increasingly have access to data-driven methods for selecting combination chemotherapy regimens—a step forward with safer, more precise, and more effective weapons in the battle against cancer.
- “MIT Professor Leverages Machine Learning to Find Promising Cancer Treatments.” MedTech Boston (2016).
- “An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer.” Bertsimas, D., O’Hair, A., Relyea, S., & Silberholz, J. Management Science Vol. 62, No. 5 (2016): 1511–1531.