Mani S, Cooper G F
Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh, USA.
Proc AMIA Symp. 2000:542-6.
Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.
医疗记录通常将调查报告、病史记录、患者诊疗情况或出院小结作为文本数据。本研究聚焦于从1611名患者的重症监护病房（ICU）出院小结中了解因果关系。识别临床状况和结果的因果因素有助于我们制定更好的管理、预防和控制策略，以改善医疗保健。为了进行因果关系发现，我们应用了局部因果发现（LCD）算法，该算法使用因果贝叶斯网络框架来表示模型变量之间的因果关系。LCD以数据集作为输入，并输出“变量Y对变量Z有因果影响”这种形式的因果关系。以出院小结中出现的词汇作为输入属性，LCD输出了8个据称的因果关系。主观上排名最有可能的关系似乎在因果关系上最合理。
