Abstract: Systems and methods are disclosed for predicting a functional impairment status. A method for predicting a functional impairment status comprises administering a battery of assessments to a patient, and receiving a plurality of response thereto; collecting performance parameters based on the patient's performance on the battery of assessments; providing the collected performance parameters and the plurality of responses to a pretrained machine learning model; predicting, using the pretrained machine learning model, a likelihood of the patient having an impairment in daily activity; determining a functional impairment status of the patient based on the prediction, and outputting the functional impairment status.

Abstract: Systems and methods are disclosed for predicting a biomarker status. A method of predicting a biomarker status comprises administering a battery of assessments to a patient; collecting multimodal data based on one or more responses to the battery of assessments from the patient; extracting one or more feature sets from the one or more response; providing the one or more feature sets to a trained machine learning model; predicting, using the trained machine learning model, a status of a biomarker of the patient; providing the prediction into a recommendation engine; determining one or more interventions based on the prediction, wherein the one or more interventions include values to the patient; and providing the one or more interventions as output.

Abstract: Systems and methods are disclosed for gaze tracking. A method includes receiving a video of a user taken by a front-facing camera of a device having a screen, receiving dimensions of the screen, parsing the video into a series of uniform-dimension video frame images, inputting the series of uniform-dimension video frame images to a pretrained artificial neural network, thereby extracting a plurality of features from the series of uniform-dimension video frame images to determine a set of internal spatial hierarchy features on each of the uniform-dimension video frame images, inputting each set of hierarchy features to a fully connected layer, the fully connected layer producing an intermediate physical estimate of the user's gaze location on the screen relative to the device camera in centimeters, determining a series of screen locations based on the intermediate physical estimate and the dimensions of the screen, and labeling the gaze location on the screen.

Abstract: According to various embodiments, a solution including methods, systems, and computer program products is provided for assessing environmental context around an individual taking an assessment. In various embodiments, a method of assessing an individual is provided. A plurality of signals, each signal from one sensor of a plurality of sensors, are received. Each signal may be associated with a modality of assessment. Each of the plurality of signals may be processed with an individualized signal processing module. A plurality of features, each from one of the processed plurality of signals, may be extracted. The plurality of features may be aggregated into a machine learning input with a feature processing module. The machine learning input may be provided to a machine learning algorithm. That environmental interference is occurring may be inferred based on the output of the machine learning algorithm.

Abstract: Systems, methods, and computer program products are provided for determining one or more biomarker and/or health condition of a target patient. In various embodiments, a method is provided where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network. The plurality of health data is derived from a plurality of modalities. A plurality of latent variables based on the plurality of health data and plurality of first order features are received from an intermediate layer of the pre-trained artificial neural network. The plurality of latent variables are provided to a pre-trained learning system. The pre-trained learning system is trained to receive as input the plurality of latent variables and output one or more biomarker and/or health condition of the target patient.

Abstract: Computational approaches to assess CNS functionality using a digital tablet and stylus are provided.