| 摘要: |
| 随着现代生活节奏的加快和生活压力的增大，越来越多的人处于一种介于健康与疾病之间的状态，现代医学称之为"亚健康"。亚健康状态如果持续时间过长且未被及时干预最终会发展成为疾病，因此识别亚健康状态并尽早干预对于预防疾病、保持机体健康状态具有重要意义。亚健康的检测方法有多种，利用医学检验技术通过实验室指标评估机体的健康状态是较为客观的评价方法。目前针对亚健康状态的实验室检测参数及参考值范围尚无明确标准，未来有望通过大数据分析结合机器学习实现对亚健康状态的科学评估。 |
| 关键词: 亚健康 检验医学 人工智能 大数据 机器学习 |
| DOI：10.16781/j.0258-879x.2019.07.0705 |
| 投稿时间：2019-02-18修订日期：2019-06-26 |
| 基金项目:上海市优秀学术带头人计划（18XD1405300），上海市东方学者跟踪计划（GZ2015009）. |
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| Laboratory medicine for diagnosis and treatment of suboptimal health: the prospect |
| JING Jie,WU Cong,LIU Shan-rong* |
(Department of Laboratory Medicine, Changhai Hospital, Naval Medical University(Second Military Medical University), Shanghai 200433, China
*Corresponding author) |
| Abstract: |
| Increasingly more people are in a state between health and disease due to different life styles, which modern medicine calls "suboptimal health". If the suboptimal health state lasts too long and not interfered in time, it will eventually progress to disease. Early identification of suboptimal health status and early intervention are important for preventing diseases and restoring a healthy state. There are many ways for detecting suboptimal health. A more objective method to diagnose suboptimal health status is by experimental indicators of laboratory medicine. However, there are difficulties in establishing the parameters and reference value ranges in laboratory detection of suboptimal health, and laboratory medicine needs to combine big data analysis and machine learning to make a scientific evaluation of suboptimal health status. |
| Key words: suboptimal health laboratory medicine artificial intelligence big data machine learning |
