Abstract: Systems and methods for determining an evaluation of one or more patients is provided. User input for evaluating one or more patients is received. A commit bundle is retrieved from a commit database. An evaluation of the one or more patients is determined based on the user input using a medical ontology configured with the retrieved commit bundle. The medical ontology is separate from the commit database. Results of the evaluation of the one or more patients are output.

Abstract: Systems and methods for determining an evaluation of one or more patients is provided. User input for evaluating one or more patients is received. A commit bundle is retrieved from a commit database. An evaluation of the one or more patients is determined based on the user input using a medical ontology configured with the retrieved commit bundle. The medical ontology is separate from the commit database. Results of the evaluation of the one or more patients are output.

Abstract: Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Abstract: A framework for personalized recommendation. An image content profile for a current case is generated. One or more auxiliary information representations associated with the current case are further generated. Affinity scores for radiology service providers are then determined by applying service profiles of the radiology service providers, the image content profile and the one or more auxiliary information representations to a trained recommendation engine. The current case is then assigned to one of the radiology service providers based on the affinity scores.

Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Abstract: Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

Abstract: Systems and methods for assessing cardiovascular disease of a patient are provided. Patient data of the patient is received. The patient data may include one or more input medical images of a chest of the patient, results of an assessment of a lung disease performed based on the one or more input medical images, demographic and clinical data of the patient, and cardiovascular imaging exams of the patient. One or more risk scores are computed for the patient based on the patient data using a trained machine learning based network.