The anatomy of the ear and the bones surrounding it are intricate yet critical for medical professionals to know. Current best practices teach ear anatomy through two-dimensional representations, which poorly characterize the three-dimensional (3D), spatial nature of the anatomy and make it difficult to learn and visualize. In this work, we describe an immersive, stereoscopic visualization tool for the anatomy of the ear based on real patient data. We describe the interface and its construction. And we compare how well medical students learn ear anatomy in the simulation compared with more traditional learning methods. Our preliminary results suggest that virtual reality may be an effective tool for anatomy education in this context.

Most of the biomedicine text mining approaches do not deal with specific cause--effect patterns that may explain the discoveries. In order to fill this gap, this paper proposes an effective new model for text mining from biomedicine literature that helps to discover cause--effect hypotheses related to diseases, drugs, etc. The supervised approach combines Bayesian inference methods with natural-language processing techniques in order to generate simple and interesting patterns. The results of applying the model to biomedicine text databases and its comparison with other state-of-the-art methods are also discussed.