Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018

Electronic health records store a lot of data about a patient. These data often include age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions. In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.
To access the methods and software, please visit the following GitHubs:

origami
varimpact
opttx

电子健康记录（Electronic health records）存储着大量患者相关数据，此类数据通常涵盖患者年龄、健康问题、当前用药情况及实验室检测结果。针对创伤后收治的患者，利用此类数据可辅助医生预测其对治疗的应答良好概率及生存几率，进而帮助医生制定更优化的诊疗决策。但在此之前，研究人员需要探索如何对数据进行整合与分析，以实现精准预测。本研究中，研究团队开发了全新的统计学分析方法以整合患者记录数据，并利用该方法预测患者的健康转归结局。随后，团队采用从医院创伤中心收集的患者健康记录数据，对其预测结果进行验证。
如需获取相关方法与软件，请访问以下GitHub仓库：
origami
varimpact
opttx