Abstract: A method for presenting a patient frequent readmission recommendation, comprising: (i) receiving patient information comprising a plurality of demographic and/or medical features; (ii) extracting the features from the information; (iii) analyzing the features to determine whether the patient is a frequent readmission patient or is at risk of being a frequent readmission patient; (iv) estimating, if the patient is determined to be a frequent readmission patient, whether the frequent readmission is due to a medical condition and/or a socioeconomic condition, or predicting a frequent readmission risk level if the patient is determined to be at risk of being a frequent readmission patient; (v) generating a recommendation based at least in part on the estimated condition or the frequent readmission risk level, wherein the recommendation comprises a medical intervention and/or a socio-behavioral intervention; and (vi) providing (180) the recommendation via a user interface.

Abstract: For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.

Abstract: For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.