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Considering Information Up-to-Dateness to Increase the Accuracy of Therapy Decision Support Systems

机译:考虑到信息的最新性,以提高治疗决策支持系统的准确性

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During the diagnostic process a lot of information is generated. All this information is assessed when making a final diagnosis and planning the therapy. While some patient information is stable, e.g., gender, others may become outdated, e.g., tumor size derived from CT data. Quantifying this information up-to-dateness and deriving consequences are difficult. Especially for the implementation in clinical decision support systems, this has not been studied. When information entities tend to become outdated, in practice, clinicians intuitively reduce their impact when making decisions. Therefore, in a system's calculations their impact should be reduced as well. We propose a method of decreasing the certainty of information entities based on their up-to-dateness. The method is tested in a decision support system for TNM staging based on Bayesian networks. We compared the actual N-state in records of 39 patients to the N-state calculated with and without decreasing data certainty. The results under decreased certainty correlated better with the actual states (r= 0.958, p=0.008). We conclude that the up-to-dateness must be considered when processing clinical information to enhance decision making and ensure more patient safety.
机译:在诊断过程中,生成大量信息。在进行最终诊断和规划治疗时评估所有这些信息。虽然一些患者信息稳定,例如,性别,其他人可能已经过时,例如,肿瘤大小来自CT数据。量化此信息的最新和导出后果很难。特别是对于临床决策支持系统的实施,这尚未研究。当信息实体往往过时的时候,在实践中,临床医生在做出决策时直观地减少了影响。因此,在系统的计算中,它们的影响也应减少。我们提出了一种根据他们的最新特征减少信息实体的确定性的方法。该方法在基于贝叶斯网络的TNM分期的决策支持系统中进行了测试。我们将39名患者记录的实际n状态与随着数据确定性的不降低的状态进行了比较了39名患者的记录。随着实际状态的确定性降低了(r = 0.958,p = 0.008)。我们得出结论,在处理临床信息时,必须考虑最新性以加强决策,并确保更多患者安全性。

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