The study of existing links among different types of medical concepts can support research on optimal pathways for the treatment of human diseases. Here, we present a clustering analysis of medical concept learned representations generated from MIMIC-IV, an open dataset of de-identified digital health records. Patient’s trajectory information were extracted in chronological order to generate +500k sequence-like data structures, which were fed to a word2vec model to automatically learn concept representations. As a result, we obtained concept embeddings that describe diagnostics, procedures, and medications in a continuous low-dimensional space. A quantitative evaluation of the embeddings shows the significant power of the extracted embeddings on predicting exact labels of diagnoses, procedures, and medications for a given patient trajectory, achieving top-10 and top-30 accuracy over 47% and 66%, respectively, for all the dimensions evaluated. Moreover, clustering analyses of medical concepts after dimensionality reduction with t-SNE and UMAP techniques show that similar diagnoses (and procedures) are grouped together matching the categories of ICD-10 codes. However, the distribution by categories is not as evident if PCA or SVD are employed, indicating that the relationships among concepts are highly non-linear. This highlights the importance of non-linear models, such as those provided by deep learning, to capture the complex relationships of medical concepts.