Abstract: One or more techniques for optimizing cancer classification based on covariate characteristics is disclosed. In a first approach, an analytics system may determine separate cutoff thresholds for positively detecting disease signal for different labels for a covariate characteristic. The system may subdivide training samples based on their labels for the covariate characteristic, to separately determine the cutoff thresholds. In other approaches, the system may train disparate classifiers for each population. The system separates the training samples based on their labels for the covariate characteristic, and separately trains classifiers to generate a signal vector representing an amount of disease signal detected in a sample. The classifiers may be trained on different feature sets as determined based on mutual information gain, genomic region coverage, and healthy activation fraction.

Abstract: Embodiments of the invention provide systems and methods for managing oncology care plans. The systems and methods involve or include: retrieving a standard care plan from an electronically stored library of oncology care plans provided by one or more clinical content providers; receiving, from a user, a modification to the standard care plan; generating a local care plan that includes the standard care plan and the modification to the standard care plan; retrieving, from the electronically stored library of oncology care plans, an updated standard care plan that includes one or more updates to the standard care plan provided by the one or more clinical content providers; and automatically generating an updated local care plan that includes the updated standard care plan and the modification to the standard care plan.