Unattended scheduled appointments, defined as patient no-shows, adversely affect both healthcare providers and patients' health, disrupting the continuity of care, operational efficiency, and the efficient allocation of medical resources. Accurate predictive modeling is needed to reduce the impact of no-shows. Although machine learning methods, such as logistic regression, random forest models, and decision trees, are widely used in predicting patient no-shows, they often rely on hard decision splits and static feature importance, limiting their adaptability to specific or complex patient behaviors. To address this limitation, we propose a new hybrid Multi-Head Attention Soft Random Forest (MHASRF) model that integrates attention mechanisms into a random forest model using probabilistic soft splitting instead of hard splitting. The MHASRF model assigns attention weights differently across the trees, enabling attention on specific patient behaviors. The model exhibited 93.72% accuracy, 94.77% specificity, 90.23% precision, 89.38% recall, a 91.54% F1 score and AUC 97.87%, demonstrated high and balance performance across metrics, outperforming decision tree, random forest, logistic regression, and naive bayes models overall. Furthermore, MHASRF was able to identify key predictors of patient no-shows using two levels of feature importance (tree level and attention mechanism level), offering deeper insights into patient no-show predictors. The proposed model is a robust, adaptable, and interpretable method for predicting patient no-shows that will help healthcare providers in optimizing resources.

翻译：患者爽约，即未按预约就诊，对医疗服务提供者和患者健康均产生不利影响，破坏了医疗服务的连续性、运营效率以及医疗资源的有效配置。为降低爽约影响，需要建立精准的预测模型。尽管逻辑回归、随机森林模型和决策树等机器学习方法已广泛应用于预测患者爽约，但它们通常依赖于硬决策分割和静态特征重要性，限制了其对特定或复杂患者行为的适应性。为解决这一局限，我们提出了一种新的混合模型——多头注意力软随机森林（MHASRF），该模型通过概率软分割而非硬分割，将注意力机制集成到随机森林模型中。MHASRF模型在不同树之间差异化分配注意力权重，从而能够关注特定的患者行为。该模型表现出93.72%的准确率、94.77%的特异性、90.23%的精确率、89.38%的召回率、91.54%的F1分数和97.87%的AUC，各项指标均显示出高且均衡的性能，整体上优于决策树、随机森林、逻辑回归和朴素贝叶斯模型。此外，MHASRF能够通过两个层次的特征重要性（树层次和注意力机制层次）识别患者爽约的关键预测因子，从而为患者爽约预测因子提供更深入的见解。所提出的模型是一种稳健、适应性强且可解释的患者爽约预测方法，将有助于医疗服务提供者优化资源配置。