The health burden posed by multifactorial and complex diseases necessitates the understanding of their relationships and multimorbidity patterns. Large-scale electronic health record (EHR) data analysis has emerged as a powerful tool for gaining insights into disease etiology and developing personalized medicine approaches. However, the inherent noise and complexity in EHR data demand robust analysis techniques. In this seminar, we present our use of multivariate statistics and network models to quantify diseasome-wide multimorbidities based on ICD codes and compare them across different EHR systems. Our findings reveal high consistency between systems, capturing known causal factors and unveiling novel multimorbidity patterns. Additionally, we present an interactive web application that enable users to examine and compare multimorbidities across multiple EHR systems effectively.