Transforming Health Care With Machine Learning
Machine Learning algorithms can process vast amounts of information and spot patterns, hallmarks of diagnosing patients and identifying risk factors.
Tepper School faculty are using machine learning methods to develop early diagnostic tools for cancer, Alzheimer's disease, and cardiac disease; treat sleep apnea; estimate surgery duration; and connect health care providers and patients using digital platforms.
Faculty Experts
Benjamin Moseley, Assistant Professor of Operations Research, Carnegie Bosch Junior Faculty Chair
Research Interests
- Scheduling theory, distributed computing, and the theoretical foundations of machine learning.
- Developing a theoretical basis for testing properties of substances, such as drugs. Improving the state of the art for discovering new drugs in practice. Applying machine learning to create better methods for the laborious and expensive process of drug discovery.
Research Interests
- Personalized medicine: Applies optimization, statistics, and machine learning tools to developing diagnostic and predictive tests for early-stage cancer, Alzheimer's, and cardiac disease.
Courses
- 47-841 Applications of High-Dimensional Statistics
Research Interests
- Internet of Things and Artificial Intelligence Platforms: Development and scaling of new digital platforms through strategic sales arrangements.
Courses
- Executive Education custom programs with health care firms.
Research Interests
- Obstructive Sleep Apnea: Use of machine learning and computer vision in treating obstructive sleep apnea.
Entrepreneurship
- medSage Technologies LLC (2002): Sold to Philips Electronics in 2010.
- NewCare Solutions LLC (2011).
Courses
- 45-906 The Business of Health Care Innovation
- Executive Education: Innovation in Health Care
Consulting
- Curavi Health
- Apollo Neuro
- Root Health
- Carigogo
- BehAlvior
- PHRQL
- AlphaLab