Understanding comorbidity patterns is crucial for improving patient outcomes and optimising healthcare strategies. In this study, we propose an approach to detect comorbidities of two diseases from clinical discharge notes. To account for the complexities of textual data, we summarise the information through propensity scores, which represent the probability of receiving a certain diagnosis conditional on the extracted text. These scores are then used as covariates in a logistic regression model to explore the association between diseases. Specifically, we compare models trained on TF-IDF weighted document-term matrices and text embeddings, employing LASSO regression, XGBoost, and multilayer perceptrons (MLP). Our results, obtained by applying this method to study the association between diabetes and Chronic Kidney Disease, demonstrate the potential of Natural Language Processing (NLP) and machine learning techniques in advancing observational healthcare research.