Sequential Inductive Transfer for Coronary Artery Disease Diagnosis

A machine lifelong learning system based on task rehearsal and multiple task learning (MTL) is used to sequentially learn a series of medical diagnostic tasks. The representations of successfully learned neural network models of the tasks are stored within a domain knowledge database. Virtual examples generated from these models are relearned, or rehearsed, in parallel with each new task using the J7MTL neural network algorithm, a variant of MTL. The ??MTL algorithm employs a separate learning rate, for each task output, k. rk varies as a function of the measure of relatedness between each prior task k and the new task being learned. Working together, the task rehearsal method and J7MTL are able to develop more accurate hypotheses for a new task by selectively transferring knowledge from related tasks in domain knowledge. Coronary artery disease data sets from three real and four fictitious hospitals provide a domain of related and unrelated tasks for testing the system. The experimental results demonstrate the method´s ability to sequentially retain and transfer clinical diagnostic knowledge when learning from impoverished training sets.