Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA.
Artif Intell Med. 2023 Mar;137:102493. doi: 10.1016/j.artmed.2023.102493. Epub 2023 Jan 31.
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.
最近因果推理技术的进展，特别是结构因果模型理论，为在因果图可识别的情况下（即可以从联合分布中恢复数据生成机制）从观察数据中识别因果效应提供了框架。然而，目前还没有这样的研究来用临床实例证明这一概念。我们提出了一个完整的框架，通过在模型开发阶段增加专家知识并进行实际的临床应用，从观察数据中估计因果效应。我们的临床应用涉及一个及时且必要的研究问题，即重症监护病房（ICU）中的氧疗干预效果。该项目的结果在各种疾病情况下都有帮助，包括 ICU 中的严重急性呼吸综合征冠状病毒 2 型（SARS-CoV-2）患者。我们使用了来自 MIMIC-III 数据库的数据，该数据库是机器学习社区中广泛使用的医疗保健数据库，包含来自马萨诸塞州波士顿 ICU 的 58976 名患者，以估计氧疗对死亡率的影响。我们还确定了模型对氧疗的特定协变量效应，以便进行更个性化的干预。
