首页> 美国卫生研究院文献>BMC Medical Informatics and Decision Making >Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)
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Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)

机译:通过集成大量临床人口统计学和实验室数据开发针对急性护理的个性化肾结石疾病诊断模型:诊断性急性护理算法-肾结石(DACA-KS)

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摘要

BackgroundKidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a ‘multi-domain’ personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information.
机译:背景肾结石(KS)病在美国患病率高,并且正在增加,并带来巨大的经济负担。 KS的诊断算法仅使用少数具有有限敏感性和特异性的变量。在这项研究中,我们测试了一种大数据方法来推断和验证KS的“多域”个性化诊断急性护理算法(DACA-KS),合并了人口统计学,生命体征,临床和实验室信息。

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