Departments of Industrial and Systems Engineering, Radiology, and Biostatistics and Medical Informatics, University of Wisconsin, 1513 University Ave., Madison, WI 53706-1572, USA.
Radiographics. 2010 Jan;30(1):13-22. doi: 10.1148/rg.301095057. Epub 2009 Nov 9.
Computer models in medical diagnosis are being developed to help physicians differentiate between healthy patients and patients with disease. These models can aid in successful decision making by allowing calculation of disease likelihood on the basis of known patient characteristics and clinical test results. Two of the most frequently used computer models in clinical risk estimation are logistic regression and an artificial neural network. A study was conducted to review and compare these two models, elucidate the advantages and disadvantages of each, and provide criteria for model selection. The two models were used for estimation of breast cancer risk on the basis of mammographic descriptors and demographic risk factors. Although they demonstrated similar performance, the two models have unique characteristics-strengths as well as limitations-that must be considered and may prove complementary in contributing to improved clinical decision making.
计算机模型在医学诊断中被开发出来，以帮助医生区分健康患者和患病患者。这些模型可以通过基于已知患者特征和临床测试结果来计算疾病可能性，从而帮助做出成功的决策。在临床风险估计中最常使用的两种计算机模型是逻辑回归和人工神经网络。进行了一项研究，以回顾和比较这两种模型，阐明每种模型的优缺点，并提供模型选择的标准。这两种模型都用于基于乳腺 X 线照片描述符和人口统计学风险因素来估计乳腺癌风险。尽管它们表现出相似的性能，但这两种模型具有独特的特征——优势和局限性——必须加以考虑，并且可能在提高临床决策方面具有互补作用。
