Ducharme N G, Pascoe P J, Lumsden J H, Ducharme G R
Department of Clinical Sciences, Cornell University, Ithaca, New York 14853.
Equine Vet J. 1989 Nov;21(6):447-50. doi: 10.1111/j.2042-3306.1989.tb02194.x.
In order to determine which variables are useful in identifying horses with abdominal pain requiring surgery, data were analysed from 219 horses presented at one veterinary teaching hospital. Using multiple stepwise discriminant analysis with a recursive partitioning algorithm, we obtained a decision tree that identifies surgical and non-surgical patients. The prevalence of surgical patients was 79 per cent in this population. The sensitivity, specificity, and positive and negative predictive values of this decision tree were 99 per cent, 55 per cent, 90 per cent and 99 per cent respectively. Compared to the clinical decision, this decision tree yielded more false positives (11 per cent) but almost eliminated false negatives (1 per cent). This decision tree was validated by the jack-knife method and also by evaluation using a new sample in a second veterinary teaching hospital in which the prevalence of surgical patients was 55 per cent. This led to sensitivity, specificity and positive and negative predictive values of 93 per cent, 73 per cent, 81 per cent and 89 per cent respectively.
为了确定哪些变量有助于识别需要手术的腹痛马匹，我们分析了一家兽医教学医院收治的219匹马的数据。使用带有递归划分算法的多步判别分析，我们得到了一个用于识别手术和非手术患者的决策树。该群体中手术患者的患病率为79%。此决策树的敏感性、特异性、阳性预测值和阴性预测值分别为99%、55%、90%和99%。与临床诊断相比，该决策树产生了更多的假阳性（11%），但几乎消除了假阴性（1%）。该决策树通过留一法进行了验证，并且在另一家兽医教学医院使用新样本进行了评估，该医院手术患者的患病率为55%。这导致敏感性、特异性、阳性预测值和阴性预测值分别为93%、73%、81%和89%。
