BMC2 Machine Learning Model and Risk Prediction Work

A digital image of a brain is superimposed over a laptop that is sitting on a white table.

The paper, Merging Machine Learning and Patient Preference: Patient-Centered Tool for Predicting Risk of Percutaneous Coronary Intervention,published in European Heart Journal will form the basis of BMC2 PCI’s risk prediction work.

Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centered tool to clearly display risks to patients and providers. Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions. A desktop version of the calculator is currently available, and an app is coming soon.

This paper garnered attention with 12 news articles by 11 outlets including TCTMD, Healio, and Medpage Today. The paper was featured in an Elsevier Practice Update and Dr. Hamilton will also share this work at an upcoming CQI Program Managers meeting. Dr. Hamilton's co-authors are Milan Seth, Ian Painter, Charles Maynard, Ravi S. Hira, Devraj Sukul, and Hitinder Gurm.