标题： 将概率树与因果网络结合用于临床和流行病学数据 显示英文标题

标题： Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data

摘要： 医疗决策需要不仅准确的预测，还需要了解各种因素如何影响患者结果。 虽然传统的机器学习（ML）模型在预测结果方面表现出色，例如识别高风险患者，但它们在回答关于干预措施的假设性问题方面存在局限。 本研究介绍了概率因果融合（PCF）框架，该框架结合了因果贝叶斯网络（CBNs）和概率树（PTrees），以超越预测。 PCF利用CBN中的因果关系来构建PTrees，从而实现对因素影响的量化和假设性干预的模拟。 PCF在三个真实世界的医疗数据集上进行了验证，即MIMIC-IV、弗雷明汉心脏研究和糖尿病数据集，这些数据集因其临床变量的多样性而被选中。 它展示了与传统ML模型相当的预测性能，同时提供了额外的因果推理能力。 为了提高可解释性，PCF结合了敏感性分析和SHapley Additive exPlanations（SHAP）。 敏感性分析量化了因果参数对结果（如住院时间（LOS）、冠心病（CHD）和糖尿病）的影响，而SHAP则突出了个体特征在预测建模中的重要性。 通过将因果推理与预测建模相结合，PCF弥合了临床直觉与数据驱动见解之间的差距。 其揭示可改变因素之间的关系并模拟假设场景的能力，为临床医生提供了对因果路径更清晰的理解。 这种方法支持更明智的、基于证据的决策，为在不同医疗环境中解决复杂问题提供了一个强大的框架。 显示英文摘要

摘要： Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional Machine Learning (ML) models excel at predicting outcomes, such as identifying high risk patients, they are limited in addressing what-if questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions. PCF leverages causal relationships from CBNs to structure PTrees, enabling both the quantification of factor impacts and simulation of hypothetical interventions. PCF was validated on three real-world healthcare datasets i.e. MIMIC-IV, Framingham Heart Study, and Diabetes, chosen for their clinically diverse variables. It demonstrated predictive performance comparable to traditional ML models while providing additional causal reasoning capabilities. To enhance interpretability, PCF incorporates sensitivity analysis and SHapley Additive exPlanations (SHAP). Sensitivity analysis quantifies the influence of causal parameters on outcomes such as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while SHAP highlights the importance of individual features in predictive modeling. By combining causal reasoning with predictive modeling, PCF bridges the gap between clinical intuition and data-driven insights. Its ability to uncover relationships between modifiable factors and simulate hypothetical scenarios provides clinicians with a clearer understanding of causal pathways. This approach supports more informed, evidence-based decision-making, offering a robust framework for addressing complex questions in diverse healthcare settings.