stowie writes: Working with Massachusetts General Hospital, MIT has developed a computational model that aims to automatically suggest cancer diagnoses by learning from thousands of data points from past pathology reports. The core idea is a technique called Subgraph Augmented Non-negative Tensor Factorization (SANTF). In SANTF, data from 800-plus medical cases are organized as a 3D table where the dimensions correspond to the set of patients, the set of frequent subgraphs, and the collection of words appearing in and near each data element mentioned in the reports. This scheme clusters each of these dimensions simultaneously, using the relationships in each dimension to constrain those in the others. Researchers can then link test results to lymphoma subtypes.
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