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在数字时代,IMPACT框架和实施可访问的in silico临床表型
Andrew Wen1,2, Huan He1, Sunyang Fu1,2
1Department of AI & Informatics, Mayo Clinic, Rochester, MN, 55905, USA.
NPJ digital medicine
|July 21, 2023
概括
本研究通过提出可重复使用和可访问的in-silico表型化的框架来解决数字健康发展的挑战. 开发的软件旨在简化临床表型化和促进算法开发.
科学领域:
- 数字健康数字健康
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 临床表型化对于数字健康应用至关重要,但传统上是耗时和昂贵的.
- 当前的in-silico表型化工具缺乏跨任务通用性,并且用户往往无法访问.
- 需要可重复使用和用户友好的in-silico表型解决方案.
研究的目的:
- 识别在体表型化采用和使用的障碍.
- 提出一个有效的in-silico表型化所需的框架.
- 引入一个软件应用程序来实现这个框架,以改善临床表型算法开发.
主要方法:
- 文献综述和对现有的in-silico表型化方法的分析.
- 基于观察到的实施挑战,制定一个框架.
- 实施软件应用程序,优先考虑易于采用和可重复使用.
主要成果:
- 确定阻碍在体表型化广泛使用的关键障碍.
- 一个拟议的框架,概述了可通用和可访问工具的基本特征.
- 一个软件应用程序的演示,以促进临床表型化算法开发.
结论:
- 一个新的框架和软件应用程序可以提高in-silico表型的效率和可访问性.
- 开发的解决方案支持跨任务的可重复使用性,解决了当前工具中存在的重大差距.
- 这项工作旨在通过简化临床数据抽象来加速数字健康应用程序的开发.
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