超越监督学习以普及医疗保健
IEEE reviews in biomedical engineering
|July 20, 2023
概括
医疗保健中的机器学习面临着数据挑战. 本综述探讨了超越监督方法的高级学习策略,以改善现实世界普遍的医疗保健应用的模型概括.
科学领域:
- 计算健康信息学 计算健康信息学
- 机器学习在医学中的应用
- 为医疗保健提供传感技术.
背景情况:
- 医疗数据的稀缺性,质量和异质性限制了监督机器学习.
- 监督学习依赖于统计学适配,这对于复杂的医疗保健数据来说是不够的.
- 无处不在的医疗保健应用需要强大的和可泛化的计算模型.
研究的目的:
- 识别应用机器学习到普遍医疗保健中的挑战.
- 审查克服完全监督学习局限性的学习策略.
- 提出数据效率高和可信赖的医疗保健的未来方向.
主要方法:
- 对超越完全监督学习的当前趋势的调查.
- 对七个关键学习策略的分析,以提高概括性.
- 确定医疗信息学领域新兴的研究方向.
主要成果:
- 强调了监督学习中经验风险最小化的局限性.
- 总结了七种不同的学习策略,以提高模型性能.
- 通过先进的方法来解决医疗保健数据的关键挑战.
结论:
- 先进的学习策略对于在普遍医疗保健中有效的机器学习至关重要.
- 未来的研究应该关注数据的效率,可扩展性和可信度.
- 利用多模式和多源传感数据对于未来的医疗保健AI至关重要.
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