Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows. Synopsis netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability. In a cancer survival prediction benchmark, netDx performs competitively or better than a diverse panel of machine‐learning algorithms. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways predictive of outcome, as demonstrated in diverse data sets (breast cancer and asthma). netDx is freely available as an R package and as a Docker image. Code, tutorials and worked examples are available at: http://netdx.org. netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability.