We propose vine copula-based classifiers for probabilistic risk prediction in perioperative settings. We obtain full joint probability models for mixed continuous-ordinal variables by fitting a separate vine copula to each outcome class, capturing nonlinear and tail-asymmetric dependence. In a cohort of 767 elective bowel surgeries (81 serious vs. 686 non-serious complications), posterior probabilities from the fitted vine classification models are used to allocate patients into low-, moderate-, and high-risk groups. Compared to weighted logistic regression and random forests with stratified sampling, the vine copula-based classifiers achieve up to 10% lower class-specific Brier scores and negative log-likelihoods on the out-of-sample. The vine copula-based classifier identifies a large cohort of true low-risk patients potentially eligible for early discharge. Scenario analyses based on the fitted vine copula models provide interpretable risk profiles, including nonlinear relationships between body mass index, surgery duration, and blood loss, which might remain undetected under linear models. These results demonstrate that vine copula-based classifiers offer a reliable and interpretable framework for individualized, probability-based patient risk profiling. As such, they represent a new, promising tool for data-driven decision-making in perioperative care.

翻译：我们提出基于藤式Copula的分类器，用于围手术期场景中的概率化风险预测。通过为每个结局类别分别拟合藤式Copula，我们建立了混合连续-有序变量的完整联合概率模型，从而捕捉非线性与尾部非对称依赖关系。在包含767例择期肠道手术（81例严重并发症与686例非严重并发症）的队列中，利用拟合的藤式分类模型所得的后验概率将患者划分为低风险、中风险与高风险组。与采用分层抽样的加权逻辑回归和随机森林方法相比，基于藤式Copula的分类器在样本外数据上实现了最高达10%的类别特异性Brier分数与负对数似然降低。该分类器识别出大量真正的低风险患者群体，这些患者可能符合早期出院条件。基于拟合藤式Copula模型的情景分析提供了可解释的风险画像，包括体重指数、手术时长与失血量之间的非线性关系——这些关系在线性模型下可能无法被检测。结果表明，基于藤式Copula的分类器为个体化、基于概率的患者风险画像提供了可靠且可解释的框架，因此代表了围手术期护理中数据驱动决策的一种新型且具有前景的工具。