Jiang Xiaoqian, Kim Jihoon, Wu Yuan, Wang Shuang, Ohno-Machado Lucila
Division of Biomedical Informatics, Department of Medicine University of California at San Diego, La Jolla, CA 92093, USA.
AMIA Annu Symp Proc. 2012;2012:1260-8. Epub 2012 Nov 3.
Prognostic models are increasingly being used in clinical practice. The benefit of adding variables (e.g., gene expression measurements) to an original set of variables (e.g., phenotypes) when building prognostic models is usually measured on a whole set of cases. In practice, however, including additional information only helps build better models for some subsets of cases. It is important to prioritize who should undergo further testing. We present a method that can help identify those patients might benefit from additional testing. Our experiments based on limited breast cancer data indicate that relatively old patients with large tumors and positive lymph nodes constitute a group for whom prognoses can be more accurate with the addition of gene expression measurements. The same is not true for some other groups.
预后模型在临床实践中的应用越来越广泛。在构建预后模型时，将变量（如基因表达测量值）添加到原始变量集（如表型）中的益处通常是在一整套病例上进行衡量的。然而，在实际应用中，纳入额外信息仅有助于为某些病例子集构建更好的模型。确定谁应该接受进一步检测非常重要。我们提出了一种方法，该方法可以帮助识别那些可能从额外检测中获益的患者。我们基于有限的乳腺癌数据进行的实验表明，肿瘤较大且淋巴结呈阳性的相对年长患者群体，通过添加基因表达测量值，其预后可以更准确。而其他一些群体则并非如此。
