用概率模型预测住院患者MRSA感染风险，助力医院防控。
Prediction of Hospital Associated Infections During Continuous Hospital Stays
- 基于概率编程构建生成模型GenHAI，捕捉住院期间MRSA检测序列变化。
- 在两个真实数据集上优于判别与生成式机器学习模型，提升预测准确率。
- 适合医院管理者用于制定感染防控策略，支持因果与反事实分析。
美国疾病控制与预防中心（CDC）于2019年将耐甲氧西林金黄色葡萄球菌（MRSA）列为严重抗药性威胁。由于共病、免疫抑制、抗生素使用以及接触污染医护人员和设备等多重因素，住院患者感染MRSA并面临生命危险的风险尤为突出。本文提出一种新型生成概率模型GenHAI，用于建模单次住院期间患者的MRSA检测结果序列。该模型基于概率编程范式，可近似回答多种预测、因果及反事实问题，为医院管理者提供重要决策支持。通过在两个真实世界数据集上与判别式和生成式机器学习模型对比，验证了其有效性。
原文摘要 · Abstract (English)
The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets.
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