Step-by-step causal analysis of EHRs to ground decision-making.

ABSTRACT: Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover all population/treatment relationships make these methods increasingly attractive for studying causal effects. However, researchers should be wary of many pitfalls. We propose and illustrate a framework for causal inference estimating the effect of albumin on mortality in sepsis using an Intensive Care database (MIMIC-IV) and comparing various sensitivity analyses to results from RCTs as gold-standard. The first step is study design, using the target trial concept and the PICOT framework: Population (patients with sepsis), Intervention (comb

SUBMITTER: Doutreligne M 

PROVIDER: S-EPMC11790099 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature