在医疗保健数据中处理缺失值:基于深度学习的归算技术的系统审查
Mingxuan Liu1, Siqi Li1, Han Yuan1
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
Artificial intelligence in medicine
|June 14, 2023
概括
深度学习 (DL) 归算方法在医疗保健研究中处理缺失数据方面表现有前途,通常表现优于传统技术. 然而,对于这些先进的DL模型来说,可移植性,可解释性和公平性仍然存在挑战.
科学领域:
- 医疗保健数据科学 数据科学
- 机器学习在医学中的应用
- 生物统计学 生物统计学
背景情况:
- 有效处理缺失数据对于可靠的临床研究和决策至关重要.
- 基于深度学习 (DL) 的归算技术越来越多地被开发用于处理复杂和多样化的数据.
- 本综述系统地评估了DL归算方法,以指导医疗保健研究人员.
结论:
- DL归算模型提供了适合特定医疗保健数据类型的多种网络结构.
- 虽然DL模型并不普遍优越,但对于特定的数据集,DL模型可以获得令人满意的结果.
- 持续的挑战包括当前DL归算技术的可移植性,可解释性和公平性.
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