精确瘤学:一项审查,以评估几个可解释的方法的解释性
Marian Gimeno1, Katyna Sada Del Real1, Angel Rubio1,2
1Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, 20009, San Sebastián, Spain.
Briefings in bioinformatics
|May 30, 2023
概括
精准医学中的机器学习模型需要对临床信任进行解释. 基于树的方法在经过测试的机器学习算法中为患者治疗决策提供了最佳的解释性.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 精准医学旨在利用患者数据量身定制治疗,但机器学习模型,特别是深度学习,往往缺乏可解释性.
- 了解和信任模型决策对于临床实施至关重要,不仅仅是预测准确性.
结论:
- 机器学习的解释性是推进精准医学治疗的关键.
- 基于树的算法被推用于临床环境中的准确性和可解释性的平衡.
- 未来的研究应该专注于提高复杂的机器学习模型的可解释性和可实施性.
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