基于生物标志物的预测模型在主动健康管理中的应用和挑战
Qiming Zhao1,2, Chen Zhang1,2, Wanxin Zhang3
1Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
生物标志物驱动的预测模型提高了疾病检测和个性化护理. 一个综合框架解决了改善临床采用和主动健康管理的实施挑战.
科学领域:
- 生物医学信息学
- 计算生物学
- 数字健康
背景情况:
- 数字技术和人工智能正在改变临床数据分析的预测模型.
- 生物标志物驱动的模型为积极的健康管理,疾病检测和个性化干预提供了潜力.
研究的目的:
- 系统地审查基于生物标志物的预测模型的作用和有效性.
- 确定阻碍实施的挑战,并提出克服这些挑战的框架.
主要方法:
- 对生物标志物驱动的预测模型的系统文献审查.
- 开发一个综合框架,重点关注数据融合,治理和可解释性.
主要成果:
- 确定了关键挑战:数据异质性,标准化问题,通用性,成本和临床翻译障碍.
- 建议的框架提高了早期查的准确性,风险分层和慢性疾病和瘤的精确诊断.
结论:
- 拟议的框架有助于将生物标志物发现纳入临床实践.
- 未来的方向包括罕见疾病,动态指标,多组学,纵向研究和边缘计算.
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