Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.
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人工智能、机器学习和深度学习用于临床结果预测。
DOI：
10.1042/etls20210246
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发表时间：
2021-12-20
影响因子：
3.8
通讯作者：
Amos CI
中科院分区：
文献类型：
--
作者：
Pettit RW;Fullem R;Cheng C;Amos CI
人工智能是一个广泛的概念，它将使用计算机执行通常需要人类完成的任务的计划分组。AI方法非常适合预测临床结果。在实践中，人工智能方法可以被认为是学习标准化输入数据的结果的函数，以便在使用新数据进行试验时产生准确的结果预测。目前用于清理、创建、访问、提取、增强和表示用于训练AI临床预测模型的数据的方法已经得到了很好的定义。使用人工智能预测临床结果是一个动态和快速发展的竞技场，新的方法和应用不断涌现。电子医疗保健记录的提取或接入以及将这些与患者遗传数据相结合是目前关注的领域，具有未来增长的巨大潜力。机器学习方法，包括随机森林和XGBoost的决策树方法，以及深度学习技术，包括深度多层和递归神经网络，提供了从高维多模态数据准确创建预测的独特能力。此外，人工智能方法正在提高我们准确预测以前难以建模的临床结果的能力，包括时间依赖性和多类结果。强大的基于AI的临床结果模型部署的障碍包括不断变化的AI产品开发界面，监管要求的特殊性，以及随着时间的推移确保模型可解释性，可推广性和适应性的限制。
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.