使用阶段树模型处理健康数据：研究曲霉菌及其他丝状真菌引起的侵袭性真菌感染
Using Staged Tree Models for Health Data: Investigating Invasive Fungal Infections by Aspergillus and Other Filamentous Fungi
AI总结：
本文展示了机器学习的阶段树模型在健康数据中的应用，通过研究曲霉菌等引起的侵袭性真菌感染风险因素，揭示了复杂的非对称依赖结构，以支持医疗决策。
摘要：
机器学习模型在医学领域日益用于研究风险因素与疾病间的关联，以辅助医生预测健康结果。本文展示了利用机器学习的阶段树模型（staged tree models）来研究健康数据中复杂的非对称依赖结构。阶段树是一类特定的生成式概率图模型，能正式建模非对称条件独立和非规则样本空间。对侵袭性真菌感染风险因素的研究展示了阶段树为支持医疗决策提供的洞见。
英文摘要：
Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in predicting health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces. An investigation of the risk factors in invasive fungal infections demonstrates the insights staged trees provide to support medical decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
