Accurately identifying patients with Right Ventricular Dysfunction (RVD) is critical for timely diagnosis and treatment, yet it remains a significant challenge in clinical practice due to the complexity and variability of echocardiography text reports. In this study, we address this crucial problem by evaluating the effectiveness of recurrent neural networks (RNNs) in classifying patients with and without RVD from echocardiography text reports. We demonstrate that our model exhibits strong performance, with high recall and precision in identifying RVD, underscoring its potential utility in clinical settings. Furthermore, we enhance the interpretability of the framework by leveraging the Local Interpretable Model-Agnostic Explanations (LIME) framework. This approach allows us to extract and validate meaningful semantic information related to RVD, providing deeper insights into the model’s local predictive capabilities and improving transparency in decision-making.