Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
This work addresses the need for practical, personalized steps to reduce CVD risk for patients, focusing on lifestyle recommendations rather than genetic insights, but it appears incremental in applying inverse classification to a specific medical domain.
The paper tackles the problem of providing personalized lifestyle recommendations to mitigate cardiovascular disease (CVD) risk by proposing a longitudinal inverse classification framework that uses historical risk and patient characteristics, resulting in significant CVD risk reduction with earlier adoption.
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in the area of personalized medicine, a field of medicine that places the patient at the center of the medical decision-making and treatment process. Many CVD-focused personalized medicine innovations focus on genetic biomarkers, which provide person-specific CVD insights at the genetic level, but do not focus on the practical steps a patient could take to mitigate their risk of CVD development. In this work we propose longitudinal inverse classification, a recommendation framework that provides personalized lifestyle recommendations that minimize the predicted probability of CVD risk. Our framework takes into account historical CVD risk, as well as other patient characteristics, to provide recommendations. Our experiments show that earlier adoption of the recommendations elicited from our framework produce significant CVD risk reduction.