Abstract: Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine-learning model to determine predicted multi-level client intent classifications and provide a graphical user interface including selectable options for the predicted multi-level client intent classifications. In particular, in one or more embodiments, the disclosed systems utilize the machine-learning model to generate predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities. The disclosed systems can provide the multi-level client intent classifications to an agent device via a graphical user interface. Moreover, the disclosed systems can make recommendations and/or take action based on the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities.

Abstract: Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted.

Abstract: Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine a predicted client disposition classification and generate an automated interaction response. For example, disclosed systems utilize the machine learning model to generate a predicted client disposition classification and a corresponding disposition classification probability. The disclosed systems can utilize the predicted client disposition classification, the disposition classification probability, and a disposition classification threshold to generate an automated interaction response that references the predicted client disposition classification. Moreover, the disclosed systems can provide the automated interaction response to a client device, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted.

Abstract: Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted. Related apparatus, systems, techniques and articles are also described.

Abstract: Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted. Related apparatus, systems, techniques and articles are also described.