We propose a novel approach for simultaneous exploratory analysis and prognosis of multiple chronic conditions that can enable healthcare professionals to harness the vast amounts of medical data available in electronic health records (EHRs) in a comprehensive manner for early treatment, the discovery of previously unknown symptoms and exploration of new associations in the context of individual chronic conditions. In particular, this manuscript focuses on the use of early clinical notes available in EHRs to diagnose multiple chronic conditions. While most methods for EHR analysis have focused on single disease predictions, multiple conditions called co morbidities tend to occur simultaneously with the primary chronic condition in many patients. To this end, we model each chronic condition as a latent variable in a novel supervised topic model which results in the prediction of multiple illnesses associated with each patient. In addition, the supervised topic model also provides an interpretable graphical visualization of each chronic condition for detailed exploratory analysis. We compare the proposed model to relevant state-of-the art methods, demonstrating both its quantitative and qualitative merits.