论文标题
用可解释的ML估计Covid-19的不连续的时变风险因素和治疗益处
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
论文作者
论文摘要
治疗方案，疾病的理解和病毒特征随着19 covid-19的大流行而发生了变化。结果，与患者合并症和生物标志物相关的风险也发生了变化。我们通过对2020年3月至2021年8月的纽约市医院系统中的4000名患者进行了与4000多名Covid-19的患者进行时间变化的观察分析，从而增加了Covid-19中有关炎症，止血和血管功能的对话。为了进行这种分析，我们在纽约市医院系统中住院的COVID-19。我们将基于树的添加剂模型与暂时的添加剂进行了分析，从而通过不断变化的方式将基于树的添加性互动恢复了不断变化的变化。我们发现，血栓形成的生物标志物越来越多地预测了从2020年3月到2021年8月，而炎症和血栓形成生物标志物之间的关联却削弱了。除了Covid-19，这还提出了一种直接的方法，以估计未知和不连续的时变效果。
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for COVID-19 in a New York City hospital system from March 2020 to August 2021. To perform this analysis, we apply tree-based generalized additive models with temporal interactions which recover discontinuous risk changes caused by discrete protocols changes. We find that the biomarkers of thrombosis increasingly predicted mortality from March 2020 to August 2021, while the association between biomarkers of inflammation and thrombosis weakened. Beyond COVID-19, this presents a straightforward methodology to estimate unknown and discontinuous time-varying effects.
