Visweswaran Shyam, Hanbury Paul, Saul Melissa, Cooper Gregory F
Center for Biomedical Informatics, University of Pittsburgh, Pennsylvania, USA.
AMIA Annu Symp Proc. 2003;2003:689-93.
Detection and prevention of adverse events and, in particular, adverse drug events (ADEs), is an important problem in health care today. We describe the implementation and evaluation of four variations on the simple Bayes model for identifying ADE-related discharge summaries. Our results show that these probabilistic techniques achieve an ROC curve area of up to 0.77 in correctly determining which patient cases should be assigned an ADE-related ICD-9-CM code. These results suggest a potential for these techniques to contribute to the development of an automated system that helps identify ADEs, as a step toward further understanding and preventing them.
不良事件尤其是药物不良事件（ADEs）的检测与预防是当今医疗保健领域的一个重要问题。我们描述了用于识别与ADE相关出院小结的简单贝叶斯模型的四种变体的实施与评估。我们的结果表明，这些概率技术在正确确定哪些患者病例应被分配与ADE相关的ICD - 9 - CM代码方面，实现了高达0.77的ROC曲线面积。这些结果表明这些技术有可能有助于开发一个有助于识别ADEs的自动化系统，作为进一步理解和预防ADEs的一步。
