Abstract: Systems and methods for using a differentiable multi-agent Actor-Critic (DiMAC) for multi-step radiology report summarization. The tasks of extracting salient sentences and phrases are divided across two collaborating agents that are trained end-to-end using reinforcement learning (RL).

Abstract: Systems and methods for generating synthetic medical data are provided. One of 1) an input medical image, 2) input medical text, or 3) an input medical image/text pair is received. Features are extracted from the received one of 1) the input medical image, 2) the input medical text, or 3) the input medical image/text pair. One of A) synthetic medical text, B) a synthetic medical image, or C) a synthetic medical image/text pair is generated for the received one of 1) the input medical image, 2) the input medical text, or 3) the input medical image/text pair respectively based on the extracted features and using a trained machine learning based model. The generated one of A) the synthetic medical text, B) the synthetic medical image, or C) the synthetic medical image/text pair is output.

Abstract: Systems and methods for generating radiology passages are provided. An input common data element and one or more associated input values are received. A radiology passage is generated based on the input common data element and the one or more associated input values using a trained language model. The generated radiology passage is output.

Abstract: Systems and methods for performing a clinical task using a trained language model are provided. Input medical data associated with a medical domain is received. A clinical task is performed based on the input medical data using a trained language model. Results of the clinical task are output. The trained language model is trained by: receiving domain-specific training data associated with the medical domain and training a pretrained, instruction-tuned language model for the medical domain using the domain-specific training data.

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 technique for generating a medical examination report from sensor data of a medical examination is provided. Sensor data from a set of sensors are received in relation to the medical examination. The sensors include a microphone. The sensor data include audio data with utterances by a medical professional and patient representative. The sensor data are processed, with the audio data transformed into text. A verbatim report of the medical examination is generating. The verbatim report includes each excerpt of the text assigned to the medical professional or to the patient representative. The verbatim report is converted into a summarizing medical examination report. The summary comprises vocabulary, and/or text excerpts, by accessing a predetermined ontology database. The medical examination report is stored in an electronic medical report database.

Abstract: Systems and methods for providing a means for improving the expressiveness and/or robustness of a machine learning system's result, based on imaging data and/or to make it possible to combine imaging data with non-imaging data to improve statements, which are deduced from the imaging data. The object is achieved by a computer implemented method, and uncertainty quantifier, medical system and a computer program product, and includes receiving a set of input data quantified as uncertainty, providing an information fusion algorithm, and applying the received set of input data on the provided information fusion algorithm, while modeling the propagation of uncertainty through the information fusion algorithm to predict an uncertainty for the medical assessment as a result (r), provided by the machine-learning system (M), based on the provided set of input data.

Abstract: Systems and methods for using a differentiable multi-agent Actor-Critic (DiMAC) for multi-step radiology report summarization. The tasks of extracting salient sentences and phrases are divided across two collaborating agents that are trained end-to-end using reinforcement learning (RL).

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 method for performing hierarchical entity classification of an entity mention within a context, wherein ontological classes are computed for the entity mention levelwise using a contextual representation of the context and a state representation obtained by running an end-to-end trained decoding recurrent neural network on a mention representation of the entity mention.

Abstract: A method and system for generating summaries of posts of interleaved text are provided. The method includes embedding, by a first neural network, each post through word-to-word encoding; embedding, by a second neural network, overall content of the plurality of posts through post-to-post encoding based on the word-to-word encoding of each post; generating, by at least a third neural network, a summary of the at least one thread through word-to-word decoding based on the overall content embedding of the plurality of posts; and displaying the summary of the at least one thread to a user.

Abstract: A method and system for generating summaries of posts of interleaved text are provided. The method includes embedding, by a first neural network, each post through word-to-word encoding; embedding, by a second neural network, overall content of the plurality of posts through post-to-post encoding based on the word-to-word encoding of each post; generating, by at least a third neural network, a summary of the at least one thread through word-to-word decoding based on the overall content embedding of the plurality of posts; and displaying the summary of the at least one thread to a user.

Abstract: A method for performing hierarchical entity classification of an entity mention within a context, wherein ontological classes are computed for the entity mention levelwise using a contextual representation of the context and a state representation obtained by running an end-to-end trained decoding recurrent neural network on a mention representation of the entity mention.