A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorganisms. The rows of the adjacency matrix correspond to biosamples, and the columns correspond to instances of metadata and drugs associated with one or more of the biosamples. The elements of the adjacency matrix are assigned non-zero numerical values or zero depending on whether a known association exists. The adjacency matrix is then factored using a selected number of latent factors, thereby producing a reconstruction matrix approximating the adjacency matrix. The values of the reconstruction matrix are used to predict antimicrobial susceptibility of a biosample ID to a drug when antibiogram data are lacking.