论文标题
基于机器学习的生物老化估计技术：一项调查
Machine Learning-based Biological Ageing Estimation Technologies: A Survey
论文作者
论文摘要
近年来，已经开发了各种估计生物年龄（BA）的方法。特别是随着机器学习（ML）的发展，BA预测的类型越来越多，精度得到了极大提高。估计BA的模型在监测健康的衰老中起着重要作用，并可以提供新的工具来检测普通人群的健康状况并向较不健康的人发出警告。我们将主要使用ML回顾三种年龄预测方法。它们基于血液生物标志物，面部图像和结构神经影像学特征。目前，使用血液生物标志物的模型是最简单，最直接，最准确的方法。面部图像方法受种族，环境等各个方面的影响，预测准确性不是很好，这不能为医疗领域做出巨大贡献。总而言之，我们在这里为我们和其他潜在的一般人口的大数据时代跟踪前进的方向，并展示了利用当今可用的大量数据的方式。
In recent years, there are various methods of estimating Biological Age (BA) have been developed. Especially with the development of machine learning (ML), there are more and more types of BA predictions, and the accuracy has been greatly improved. The models for the estimation of BA play an important role in monitoring healthy aging, and could provide new tools to detect health status in the general population and give warnings to sub-healthy people. We will mainly review three age prediction methods by using ML. They are based on blood biomarkers, facial images, and structural neuroimaging features. For now, the model using blood biomarkers is the simplest, most direct, and most accurate method. The face image method is affected by various aspects such as race, environment, etc., the prediction accuracy is not very good, which cannot make a great contribution to the medical field. In summary, we are here to track the way forward in the era of big data for us and other potential general populations and show ways to leverage the vast amounts of data available today.
