所以你有很高的AUC,现在怎么办? 在将机器学习模型从计算机带到床边时,重要考虑因素的概述
Jiawen Deng1, Mohamed E Elghobashy1, Kathleen Zang2
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
概括
本教程指导临床医生在医疗保健中实施机器学习 (ML) 模型. 它涵盖了重要的因素,如校准,偏差缓解和验证,以获得成功的临床应用和改善患者护理.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 健康 数据科学 数据科学
背景情况:
- 机器学习 (ML) 模型为个性化,数据驱动的临床决策提供了变革性的潜力.
- 将ML成功整合到临床实践中,需要解决不仅仅是预测准确性的因素.
研究的目的:
- 为临床医生提供关于开发和实施临床适用的机器学习分类模型的全面指南.
- 突出 ML 模型开发与现实世界医疗保健应用之间的差距.
主要方法:
- 评估和改进模型校准的方法的概述.
- 讨论选择适当的决策门,并提高模型的解释性.
- 识别和减轻偏见,强有力的验证和现实世界的测试的策略.
主要成果:
- 临床医生可以了解ML模型部署的关键方面,包括校准,值,可解释性和偏差.
- 提供了关于强大的验证技术和战略的指导,用于ML模型的现实世界测试.
- 该教程强调了在医疗保健中使用 ML 工具的可访问性和实际应用的重要性.
结论:
- 遵守本指南对于机器学习模型的有效临床部署至关重要.
- 处理校准,可解释性,偏差和验证,确保ML模型可靠和值得信赖.
- 这些ML模型的成功实施可以显著提高患者护理结果.
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