Abstract: Systems and methods are provided for wrapping a machine-learned model to facilitate interactive input acquisition. One or more computing devices can obtain a machine-learned model configured to generate a prediction based at least in part on input feature data. The computing device(s) can obtain a first input value for a first input feature of the first machine-learned model. Based at least in part on the first input value, the computing device(s) can determine an estimated value of obtaining at least one additional input value for a second input feature of the first machine-learned model. Based on the estimated value, the computing device(s) can determine whether to obtain the at least one additional input value. Using the first machine-learned model, the computing device(s) can determine a prediction based at least on the first input value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.