METHODS AND SYSTEMS FOR PREDICTING IN-VIVO RESPONSE TO DRUG THERAPIES

A method building models for predicting patient response to drug therapies uses patient data, including functional data, clinical data, and, in some implementations, genetic data (e.g., DNA extracted from diseased tissue). The functional data includes initial cell viability and cell viability in response to exposure to one or more drug therapies, and the clinical data includes patient information over time. For each patient, the method forms a feature vector comprising the functional data and the clinical data (and genetic data, when used). The method uses at least a subset of the feature vectors to train a first model to predict individual patient response to a first drug therapy. The method then stores the trained first model in a database for subsequent use in predicting patient response to the first drug therapy. Another method predicts patient responses to one or more drug therapies using the trained models.