Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification

TL;DR

This paper introduces a personalized recommendation framework called longitudinal inverse classification that suggests lifestyle changes to reduce cardiovascular disease risk by considering patient history and characteristics.

Contribution

It presents a novel longitudinal inverse classification method for personalized CVD risk mitigation, integrating historical data and patient-specific factors.

Findings

Early adoption of recommendations significantly reduces CVD risk.

The framework effectively personalizes lifestyle suggestions based on patient history.

Experimental results demonstrate the potential for meaningful risk reduction.

Abstract

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.