Explainable Artificial Intelligence for Predictive Modeling in Healthcare.
Explainable Artificial Intelligence for Predictive Modeling in Healthcare.
复制标题
可解释的人工智能在医疗保健中的预测建模。
DOI：
10.1007/s41666-022-00114-1
复制
发表时间：
2022-06
影响因子：
5.9
通讯作者：
Yang CC
中科院分区：
文献类型：
--
作者：
Yang CC
人工智能背后的原理是模仿人类智能，通过从各种来源获取的数据中学习来执行任务，识别模式或预测结果。人工智能和机器学习算法已广泛用于自动驾驶、电子商务和社交媒体中的推荐系统、金融科技、自然语言理解和问答系统。人工智能也在逐渐改变医疗保健研究的格局（Yu et al. in Biomed Eng 2：719-731）。半个世纪以来，基于规则的医学诊断方法以其对医学知识的整理和对决策规则的构造为基础，在疾病诊断和临床决策支持方面受到了广泛的关注。近年来，可以解释特征之间复杂相互作用的机器学习算法（例如深度学习）被证明在医疗保健预测建模中很有前途（Deo in Circulation 132：1920-1930，）。虽然这些人工智能和机器学习算法中的许多算法可以实现非常高的性能，但由于这些算法中的一些算法缺乏可解释性，因此通常难以在实际临床环境中完全采用。可解释的人工智能（XAI）正在出现，以帮助内部决策，行为和行动与医疗保健专业人员的沟通。通过解释预测结果，XAI获得了临床医生的信任，因为他们可以学习如何在实际情况中应用预测模型，而不是盲目地遵循预测。由于医学知识的复杂性，仍然有许多场景需要探索如何使XAI在临床环境中有效。
The principle behind artificial intelligence is mimicking human intelligence in the way that it can perform tasks, recognize patterns, or predict outcomes through learning from the acquired data of various sources. Artificial intelligence and machine learning algorithms have been widely used in autonomous driving, recommender systems in electronic commerce and social media, fintech, natural language understanding, and question answering systems. Artificial intelligence is also gradually changing the landscape of healthcare research (Yu et al. in Biomed Eng 2:719–731,). The rule-based approach that relied on the curation of medical knowledge and the construction of robust decision rules had drawn significant attention in diagnosing diseases and clinical decision support since half a century ago. In recent years, machine learning algorithms such as deep learning that can account for complex interactions between features is shown to be promising in predictive modeling in healthcare (Deo in Circulation 132:1920–1930,). Although many of these artificial intelligence and machine learning algorithms can achieve remarkably high performance, it is often difficult to be completely adopted in practical clinical environments due to the lack of explainability in some of these algorithms. Explainable artificial intelligence (XAI) is emerging to assist in the communication of internal decisions, behavior, and actions to health care professionals. Through explaining the prediction outcomes, XAI gains the trust of the clinicians as they may learn how to apply the predictive modeling in practical situations instead of blindly following the predictions. There are still many scenarios to explore how to make XAI effective in clinical settings due to the complexity of medical knowledge.
登录
查看更多内容
影响因子：
5.9
作者：
Nasajpour M;Pouriyeh S;Parizi RM;Dorodchi M;Valero M;Arabnia HR
通讯作者：
Arabnia HR
影响因子：
14.4
作者：
Miller, Tim
通讯作者：
Miller, Tim
DOI：
10.1073/pnas.2020620118
发表时间：
2021-06-15
影响因子：
11.1
作者：
Elul, Yonatan;Rosenberg, Aviv A.;Yaniv, Yael
通讯作者：
Yaniv, Yael
影响因子：
4.6
作者：
Jia, Yanfeng;Zeng, Yanli;Meng, Lingpeng
通讯作者：
Meng, Lingpeng
影响因子：
2.1
作者：
Xu, Jingjing;Fu, Chao;Nie, Wei
通讯作者：
Nie, Wei