The High-benefit Approach: A New Targeting Strategy in Precision Medicine

抄録

In medicine, treatment or intervention is typically prioritized for individuals at high risk of diseases or mortality. This high-risk approach, which focuses on "high-risk" patients, has been a cornerstone of clinical decision-making. Additionally, recent advancements in precision medicine, especially those using multi-omics data to assess disease risk, have frequently been framed within this approach. However, it is not always the case that individuals at high risk of disease benefit most from the treatment of interest. In this context, we previously proposed a novel targeting strategy called the "high-benefit approach." By applying machine learning technologies to data from randomized controlled trials or observational studies, this strategy allows us to identify subpopulations likely to experience substantial benefits from the treatment and to target them, rather than those who are simply at high risk of diseases. The high-benefit approach contributes not only to effective resource allocation but also to the potential mitigation of health disparities by identifying individuals with limited or no benefits and offering alternative approaches that would be effective for them. This review paper explains the overall concept of the high-benefit approach and its potential for application in healthcare literature, particularly in advancing future precision medicine and public health.