Exploring potential disease risk factors is critical for precision medicine, enabling the identification of biomark-ers and supporting accurate diagnostic tools and interventions. However, traditional statistical and machine learning methods often fail to reduce disease risk effectively, as they focus on correlations while ignoring essential causal relationships. Causality, on the other hand, could identify causal rela-tionships and predict effects from large-scale observational data, discovering underlying data generation mechanisms. Therefore, we propose a novel Causality Driven Analysis Framework (CDAF) to identify causal risk factors from obser-vational data. CDAF employs a converging causality growth algorithm to discover essential causal relationships through Bayesian scoring criterion, modeled as a causal graph. It estimates the average treatment effects (ATE) of direct causal edges using causal inference, ranking the importance of risk factors. CDAF demonstrated excellent performance on both benchmark datasets and other real clinical datasets. We em-pirically demonstrate that our framework offers a powerful tool for uncovering complex disease mechanisms, advanc-ing precision medicine, and enabling personalized treatment strategies.

Clinical diagnosis for complex disease conditions is a complicated decision process involving systematic inference and differentiation. Artificial Intelligence (AI) models have been a widely established approach to help improve the efficiency of various kinds of clinical decision tasks (e.g., diagnosis, treatment, and prognosis). However, due to the critical requirement of time efficiency, lack of sufficient information, and high probability of comorbid diseases in Outpatient and Emergency Settings (OES), it is still challenging to build clinically feasible AI models using the free text clinical records in OES for complex disease conditions, such as neurosurgery. Here we propose an AI diagnosis model, named LLM4DEU, for neurosurgery disease differentiations by fine-tuning a large language model (i.e., ChatGLM) using the Department of Neurosurgery, the Beijing Tiantan Hospital OES electronic health records. LLM4DEU obtained state-of-the-art performance on clinical diagnosis with a F1 score of 78.53%, which is superior to five well-known baselines (including deep learning models). In addition, we evaluated the actual performance of the model by case studies on the diagnosis of specific neurosurgical diseases (e.g., subdural hematoma, cerebral hemorrhage, and cerebral infarction). The experimental results show that the LLM4DEU model has significant advantages in diagnosing low-incidence disease conditions, and comparative analyses with clinical experts confirm the predictive power of the model in neurosurgical diagnosis.