Using Machine Learning to Improve Care of Chronically Ill Patients
Providing care for chronically ill patients, like those with end stage renal disease, presents several unique opportunities for data scientists. The frequency of treatment and volume of data collected, combined with the number of health complications in the population, presents fertile ground for high-impact predictive models. At Fresenius Medical Care, we use natural language processing and machine learning models to predict which patients are likely to miss a treatment, which patients are at high risk of hospitalization, and which patients are likely to have specific conditions.
Tommy is currently Data Science Lead at Fresenius Medical Care North America. His team uses data science broadly across the organization to create predictive models and advanced analytics support for a diverse set of company needs. Central to his team’s goals is the use of machine learning to improve medical care of the many chronically ill patients Fresenius provides care for. Tommy holds a Bachelor’s in Computer Science from the University of Waterloo, completed his PhD at the University of Rochester in Brain and Cognitive Sciences, and performed his post-doctoral research at Harvard University.