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Wikipedia-Based Automatic Diagnosis Prediction in Clinical Decision Support Systems

机译:临床决策支持系统中基于维基百科的自动诊断预测

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摘要

When making clinical decisions, physicians often consult biomedical literatures for reference. In this case, an effective clinical decision support system, provided with a patient’s health information, should be able to generate accurate queries and return to the physicians with useful articles. Related works in the Clinical Decision Support (CDS) track of TREC 2015 demonstrated the usefulness of knowing patients’ diagnosis information for supporting more effective retrieval, but the diagnosis information is often missing in most cases. Furthermore, it is still a great challenge to perform large-scale automatic diagnosis prediction. This motivates us to propose an automatic diagnosis prediction method to enhance the retrieval in a clinical decision support system, where the evidence for the prediction is extracted from Wikipedia. Through the evaluation conducted on 2014 CDS tasks, our method reaches the best performance among all submitted runs. In the next step, graph structured evidence will be integrated to make the prediction more accurate.
机译:在做出临床决定时,医师通常会查阅生物医学文献以供参考。在这种情况下,提供了患者健康信息的有效临床决策支持系统应该能够生成准确的查询,并通过有用的文章返回给医生。 TREC 2015的临床决策支持(CDS)跟踪中的相关工作表明,了解患者的诊断信息有助于支持更有效的检索,但大多数情况下通常缺少诊断信息。此外,执行大规模自动诊断预测仍然是巨大的挑战。这促使我们提出一种自动诊断预测方法,以增强在临床决策支持系统中的检索,在该系统中,用于预测的证据是从Wikipedia中提取的。通过对2014年CDS任务进行的评估,我们的方法在所有提交的运行中均达到了最佳性能。下一步,将整合图结构化证据以使预测更加准确。

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