Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
复制标题
利用人工智能对儿科疾病进行评估和准确诊断
DOI：
10.1038/s41591-018-0335-9
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发表时间：
2019-03-01
期刊：
影响因子：
82.9
通讯作者：
Xia, Huimin
中科院分区：
文献类型：
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作者：
Liang, Huiying;Tsui, Brian Y.;Xia, Huimin
基于人工智能 (AI) 的方法已成为改变医疗保健的强大工具。尽管机器学习分类器（MLC）已经在基于图像的诊断中表现出强大的性能，但对多样化和海量的电子健康记录（EHR）数据的分析仍然具有挑战性。在这里，我们表明 MLC 可以以类似于医生使用的假设演绎推理的方式查询 EHR，并发现以前的统计方法尚未发现的关联。我们的模型应用自动化自然语言处理系统，利用深度学习技术从 EHR 中提取临床相关信息。总共分析了来自 1,362,559 名儿科患者到主要转诊中心就诊的 1.016 亿个数据点，以训练和验证该框架。我们的模型在多个器官系统中表现出较高的诊断准确性，在诊断常见儿童疾病方面可与经验丰富的儿科医生相媲美。我们的研究为实施基于人工智能的系统提供了概念证明，作为帮助医生处理大量数据、增强诊断评估并在诊断不确定或复杂的情况下提供临床决策支持的手段。尽管这种影响在医疗保健提供者相对短缺的地区可能最为明显，但这种人工智能系统的好处可能是普遍的。
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.