Causal inference model for accurate medical diagnosis in Coronary Artery Bypass Graft operation

Abstract

Coronary Artery Bypass Grafting (CABG) is the most commonly performed cardiac surgery. Predicting postoperative complication risks for patients undergoing CABG is crucial for medical professionals. Considering the susceptibility of traditional models to confounding factors and the scarcity of medical data, it is necessary to design a model that can truly capture the cause-and-effect relationship between the disease and its underlying causes and achieve high accuracy even with limited data. In this paper, a novel Causal Inference Operation Risk Predictor (CIORP) is proposed. We construct a Structural Causal Model (SCM) that demonstrates how two confounders influence the model’s predictions. Then we utilize the backdoor adjustment strategy to control potential confounders from pre-operative information and non-causal intraoperative data. In parallel, capitalizing on few-shot learning techniques, we initiate pre-training using categories with ample samples to extract essential features. Subsequently, we fine-tuned our model on sparse sets of labeled data, facilitating accurate predictions in scenarios with limited annotated samples. The experimental outcomes demonstrate that our model surpasses most existing methods in the internal Electronic Health Record (EHR) of CABG patients, effectively predicting low cardiac output, new-onset atrial fibrillation, perioperative myocardial infarction, and cardiac arrest or ventricular fibrillation post-operation. Our work effectively mitigates the impact of confounding factors, allowing the model to make accurate predictions with minimal medical data.