在精神病学研究和临床实践中使用机器学习模型的挑战
Dijana Ostojic1, Paris Alexandros Lalousis2, Gary Donohoe1
1School of Biological and Chemical Sciences and School of Psychology, Centre for Neuroimaging, Cognition and Genomics (NICOG), University of Galway, Ireland.
概括
机器学习 (ML) 通过分析各种数据,为了解复杂的精神疾病提供了强大的工具. 本综述解决了将ML应用于精神病学的关键挑战,提供了改善模型可靠性和可解释性的解决方案.
科学领域:
- 神经科学和精神病学 在
- 计算生物学和机器学习
背景情况:
- 精神疾病是复杂的和异质的,需要整合不同的数据类型 (临床,神经成像,基因组,环境) 的生物学理解.
- 传统的统计方法在分析精神病学研究中常见的大型多维数据集方面存在局限性.
研究的目的:
- 检查在精神病学研究和临床实践中应用机器学习 (ML) 模型的挑战.
- 为减轻这些挑战提供实际指导和最佳实践.
- 提高精神病学ML模型的可靠性和可解释性.
主要方法:
- 审查现有的关于机器学习在精神病学中的应用文献.
- 识别和深入研究 ML 模型开发和部署中常见的挑战.
- 详细介绍了克服已确定的挑战的实际解决方案和最佳实践.
主要成果:
- 确定了关键挑战,包括维度的诅咒,数据质量问题,黑子问题,超参数调整,外部验证,类失衡和数据代表性.
- 提出实际解决方案和最佳实践,以应对每一个已识别的挑战.
- 强调了这些建议的潜力,以改善精神病学背景下的ML模型性能.
结论:
- 机器学习具有很大的潜力,可以促进精神疾病的理解,诊断,预后和治疗.
- 解决精神病学ML应用的具体挑战对于成功实施至关重要.
- 实施推的最佳实践可以导致更可靠和可解释的ML模型,促进它们的临床实用性.
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