确保基于人工智能的公平,安全和可解释的预测工具在现实世界瘤环境中
Renee George1, Benjamin Ellis1, Andrew West1
1The Ronin Project, San Mateo, CA, USA.
Communications medicine
|June 22, 2023
概括
这项研究开发了一种人工智能模型,用于预测癌症患者在30天内访问急诊室的情况. 该模型表现出卓越的性能和稳定性,可实现积极的风险管理.
科学领域:
- 在瘤学中使用人工智能
- 在医疗保健中的预测建模.
- 医疗信息学 医疗信息学
背景情况:
- 癌症治疗可能会导致需要急诊室 (ED) 参观的症状.
- 开发预测模型可以帮助识别高风险患者.
- 积极监测人工智能模型对于可靠的性能至关重要.
研究的目的:
- 开发和验证基于人工智能的预测模型,用于癌症患者的30天ED访问.
- 在现实环境中展示一种主动的方法来监测AI模型.
- 评估模型在不同患者群体的性能和稳定性.
主要方法:
- 使用例行收集的电子健康记录数据.
- 开发了一个变量自编码器k-最近邻居 (VAE-kNN) 算法.
- 在77天的生产期内,对28369名患者的84,138个观察结果进行了评估.
主要成果:
- VAE-kNN算法实现了一个特殊的接收器运行特征曲线 (AUC) 下的面积为0.80.
- 在人口和疾病组中,模型性能保持稳定 (AUC 0.74-0.82).
- 监测过程有效地检测到数据源问题,为未来的业绩提供了洞察力.
结论:
- 开发的算法准确地预测了癌症患者30天ED访问的风险.
- 模型输出被证实是公平的,并且随着时间的推移而稳定.
- 积极的监测方法确保了AI预测模型在临床实践中的可靠性.
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