Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification

Autor:	Lash, Michael T., Street, W. Nick
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: Forthcoming in AIBH 2020 (a BIBM 2020 workshop)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.08254 Zobrazit plný text záznamu View this record from Arxiv