Abstract: A framework for generating reasons for imaging studies. An extractor, including a reinforcement learning agent, is trained to select one or more relevant sentences from the training histories of present illness. An abstractor is further pre-trained to generate one or more reasons for study from the one or more relevant sentences. An entity linking system is pre-trained using medical text corpora to map one or more mentions in the one or more reasons for study to one or more standardized medical entities for predicting one or more diagnoses. The reinforcement learning agent may then be re-trained using one or more rewards generated by the entity linking system. One or more reasons for study may be generated from a current history of present illness using the trained extractor, abstractor and entity linking system.

Abstract: A framework for generating reasons for imaging studies. An extractor, including a reinforcement learning agent, is trained to select one or more relevant sentences from the training histories of present illness. An abstractor is further pre-trained to generate one or more reasons for study from the one or more relevant sentences. An entity linking system is pre-trained using medical text corpora to map one or more mentions in the one or more reasons for study to one or more standardized medical entities for predicting one or more diagnoses. The reinforcement learning agent may then be re-trained using one or more rewards generated by the entity linking system. One or more reasons for study may be generated from a current history of present illness using the trained extractor, abstractor and entity linking system.