Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A wellness management application enables automated diagnosis of a throat infection based on a throat image. A captured image is segmented into a plurality of image segments corresponding to different anatomical structures. A set of machine learning models are applied to the respective image segments to generate respective prediction scores indicative of likelihood of infection. Each of the set of machine learning models are independently trained based on labeled images of the corresponding anatomical structure. The results of the respective models may be aggregated to generate an aggregate prediction. Furthermore, various visual representations may be generated that illustrate respective contributions of different regions of the image to the prediction. The wellness management application may be integrated with a telehealth system to facilitate diagnosis and treatment of infections.

Abstract: The system can include a plurality of data processing modules (e.g., machine learning models, rule-based models, etc.), which can include: a feature generation module, a Driver versus Passenger (DvP) classification module, a validation module, an update module, and/or any other suitable data processing modules. The system can optionally include a mobile device (e.g., such as a mobile cellular telephone, user device, etc.) and/or can be used in conjunction with a mobile device (e.g., receive data from an application executing at the mobile device and/or utilize mobile device processing, etc.). However, the system can additionally or alternatively include any other suitable set of components. The system functions to facilitate execution of method S100. Additionally or alternatively, the system functions to classify a role of a mobile device user (e.g., driver or passenger; driver or nondriver; etc.) for a vehicle trip and/or determine a semantic label for the vehicle trip.

Abstract: An equine health monitoring system generates health predictions based on monitored sensor data from at least one wearable device worn on a leg of an equine. The wearable device includes a pad and one or more securing straps for securing the pad to the leg of the equine. The pad includes an array of spatially distributed sensors for obtaining sensor data and a controller for transmitting the sensor data to a prediction application of a client device. The predication application applies a machine learning model to the sensor data and equine profile to generate likelihood values associated with one or more equine health conditions. The prediction application generates from the likelihood values, a prediction associated with the one or more equine health conditions, and outputs a visual representation of the prediction to a user interface of the client device.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: The system can include a plurality of data processing modules (e.g., machine learning models, rule-based models, etc.), which can include: a feature generation module, a Driver versus Passenger (DvP) classification module, a validation module, an update module, and/or any other suitable data processing modules. The system can optionally include a mobile device (e.g., such as a mobile cellular telephone, user device, etc.) and/or can be used in conjunction with a mobile device (e.g., receive data from an application executing at the mobile device and/or utilize mobile device processing, etc.). However, the system can additionally or alternatively include any other suitable set of components. The system functions to facilitate execution of method S100. Additionally or alternatively, the system functions to classify a role of a mobile device user (e.g., driver or passenger; driver or nondriver; etc.) for a vehicle trip and/or determine a semantic label for the vehicle trip.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A device (e.g., an application-specific integrated circuit chip) includes a memory module processing unit and an interface. The memory module processing unit is configured to receive an instruction to obtain values stored in one or more memory components and process the obtained values to return a processed result. The memory module processing unit is also configured to store the obtained values in a cache based on one or more criteria. The memory module processing unit is configured to be included on a computer memory module configured to be installed in a computer system. The interface is configured to communicate with the one or more memory components included on the computer memory module.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: Automated digital content layout systems and techniques are described. Interaction of a creative professional with a digital content design system to create a digital content design is monitored. The digital content design is created by the creative professional in accordance with a maximum amount of display area along a primary axis to be used to display the digital content design. The digital content design system then employs a layout system to generate a layout definition for inclusion as part of the digital content design that includes a set of rules to control how the items of digital content are arranged in a user interface in amounts of the display area along the primary axis that are less than this maximum amount.

Abstract: A device (e.g., an application-specific integrated circuit chip) includes a memory module processing unit and an interface. The memory module processing unit is configured to receive an instruction to obtain values stored in one or more memory components and process the obtained values to return a processed result. The memory module processing unit is also configured to store the obtained values in a cache based on one or more criteria. The memory module processing unit is configured to be included on a computer memory module configured to be installed in a computer system. The interface is configured to communicate with the one or more memory components included on the computer memory module.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: Automated digital content layout systems and techniques are described. Interaction of a creative professional with a digital content design system to create a digital content design is monitored. The digital content design is created by the creative professional in accordance with a maximum amount of display area along a primary axis to be used to display the digital content design. The digital content design system then employs a layout system to generate a layout definition for inclusion as part of the digital content design that includes a set of rules to control how the items of digital content are arranged in a user interface in amounts of the display area along the primary axis that are less than this maximum amount.