A Learning Health Care System Using Computer-Aided Diagnosis.
A Learning Health Care System Using Computer-Aided Diagnosis.
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DOI：
10.2196/jmir.6663
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发表时间：
2017-03-08
影响因子：
7.4
通讯作者：
Cimino JJ
中科院分区：
文献类型：
--
作者：
Cahan A;Cimino JJ
医生在评估病人时直观地应用模式识别。合理的诊断需要将临床模式置于疾病先验概率的背景下，然而医生经常表现出有缺陷的概率推理。诊断的困难反映在致命和昂贵的诊断错误的高发生率上。60年前推出的计算机化诊断支持系统仍然没有被内科医生广泛使用。这些系统不能有效地识别模式，也不能考虑潜在诊断的基本率。我们回顾了当前计算机辅助诊断支持系统的局限性。然后，我们描绘了未来的诊断支持系统，并为其发展提供了一个概念框架。我们主张使用一种新的临床图像知识表示模型来获取医生知识。该模型（基于结构化的患者表现模式）不仅包含症状和体征，还包含它们的时间和语义相互关系。我们呼吁收集众包的、自动识别的、结构化的患者模式，作为支持分布式知识积累和维护的手段。在这种方法中，每个结构化的患者模式都增加了一个自我增长和维护的知识库，分享了全世界医生的经验。除了通过将症状和体征与记录的最终诊断相关联来支持诊断外，集体模式图还可以提供疾病基础率估计和实时监测，以便及早发现疫情。我们解释了在资源有限的环境中，医疗保健如何从使用这种方法中受益，以及如何将其应用于为学生和从业人员提供反馈丰富的医学教育。
Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.