Unità di Ingegneria della Conoscenza Clinica, Laboratorio di Epidemiologia Clinica, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri, 1, 20156 Milano, Italy.
Artif Intell Med. 2012 May;55(1):1-11. doi: 10.1016/j.artmed.2011.11.007. Epub 2011 Dec 29.
Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports.
Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms.
The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor.
The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge.
在医院信息系统中设置临床报告，使得记录各种临床表现成为可能。有向无环图（Dag）为表示临床问题领域中的因果关系提供了一种有用的方法，并且是许多在医学文献中描述的概率模型（如贝叶斯网络）的核心。然而，医学从业者通常没有接受过引出 Dag 特征的培训。部分困难在于在选择特定患者所有感兴趣的因果变量之前应用直接因果关系的概念。我们设计了一种自动化访谈，以指导医生开发 Dag 来表示他们对临床报告的理解。
分析医学概念，以找到可以通过支持引出因果 Dag 的算法遵循的医学推理模式。定义临床相关性，以通过将专家的注意力引导到与已插入图中的节点相关的变量上来帮助制定仅相关的问题。描述了所提出的访谈的关键程序特征的四个算法。
自动化访谈包含有关医学概念的问题，以医学术语表述。第一个引出会话提出了有关患者主要抱怨和与作为诊断假设的疾病相关的结果、其可观察表现和风险因素的问题。第二个会话重点关注通过考虑综合征、功能障碍、致病异常、偏差和效应修饰符来细化初始因果路径的问题。一个关于胃肠问题的案例研究和一个关于感染患者的案例研究说明了算法根据医生提供的答案产生的输出。
所提出的引出框架的特点是与医学背景具有很强的一致性，并逐步引入相关的医学主题。通过将收集到的答案与接受的生物医学知识来源中包含的证据进行匹配，对主观引出的 Dag 进行修订和测试。
