Abstract: Certain aspects of the present disclosure provide techniques for predicting electronic health record data using ML. This includes determining a plurality of attributes of a textual description of a patient medical examination, including: detecting the plurality of attributes based on analyzing the textual description using a first ML model trained to parse patient textual descriptions. This further includes predicting a change to an electronic health record for the patient, including: providing to a second ML model the plurality of attributes of the textual description and patient medical data for the patient, where the second ML model is trained to predict changes to patient electronic health records based on attributes of textual data relating to the patient and patient medical data. The predicted change is provided to an electronic system to change the electronic health record and affect medical treatment for the patient.

Abstract: Example multi-channel display systems are disclosed. An example apparatus includes at least one processor to execute instructions to at least in response to determining that a first display controller is to provide at least a first portion of video data on a first channel, provide a first control signal to the first display controller instructing the first display controller to provide the at least the first portion of the video data on the first channel, and in response to determining that a second display controller is to provide at least a second portion of the video data on a second channel, provide a second control signal to the second display controller instructing the second display controller to provide the at least the second portion of the video data on the second channel, the second channel different from the first channel.

Abstract: Techniques for improved machine learning are provided. Resident data describing a first condition of a first resident of a residential care facility is received. An optimized treatment plan is selected for the first resident by extracting a first plurality of resident attributes, from the resident data, generating a first approach to remediate the first condition, and generating a first predicted recovery score by inputting the first approach and the first plurality of resident attributes into a trained machine learning model. The optimized treatment plan is implemented for the first resident.

Abstract: Techniques for improved computer modeling are provided. Resident data describing a resident of a healthcare facility is received, and a set of resident attributes corresponding to a defined set of features is extracted from the resident data. An acuity score is generated for the resident by processing the set of resident attributes using an acuity model, the acuity model specifying a respective weight for each respective feature in the defined set of features. One or more interventions are initiated for the resident based on the acuity score.

Abstract: Techniques for improved machine learning are provided. User data describing a user is received, and a set of user attributes corresponding to a defined set of features is extracted from the user data. A risk score is generated by processing the set of user attributes using a trained machine learning model, where the risk score indicates a probability that the user has or will develop depression. One or more interventions are initiated for the user based on the risk score.

Abstract: Techniques for improved machine learning are provided. Patient data describing a patient is received, and a medication to be evaluated with respect to the patient is identified. An efficacy score is generated by processing at least a subset of the patient data using a hybrid machine learning model comprising a static portion and a dynamic portion, where the efficacy score indicates predicted efficacy of the medication for the patient. The medication is provided for the patient based at least in part on the efficacy score.

Abstract: Embodiments of the present disclosure relate to parallel mask rule checking on evolving mask shapes in optical proximity correction (OPC) flows for integrated circuit designs. Systems and methods are disclosed that perform mask (manufacturing) rule checks (MRC) in parallel, sharing information to maintain symmetry when violations are corrected. In an embodiment the shared information is also used to minimize changes to the geometric area of proposed mask shapes resulting from the OPC. In contrast to conventional systems, MRC is performed for multiple edges in parallel, sharing information between the different edges to encourage symmetry. In an embodiment, all edges may be adjusted in parallel to reduce mask-edge traversal bias.

Abstract: The disclosure relates to system and method for providing assistance to a user using augmented reality. The method includes acquiring a video stream and a set of data associated with a task being performed by a user, in real-time, using a camera and/or a sensor device. The video stream includes sequential frames. The method further includes determining a present state associated with the task based on the sequential frames using an Artificial Neural Network (ANN) based action prediction model; determining scenarios and events corresponding to the scenarios based on the video stream and the set of data using an ANN based augmented intelligence model; and determining sequential instructions required for assisting the user to accomplish the task, dynamically, based on the present state and the events associated with the task, using at least one of a rule-based engine and an ANN based instruction prediction model.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Systems, methods, and other embodiments associated with automatically expanding channels for bots are described. In one embodiment, a system includes a bot repository configured to identify each bot of a plurality of bots available in the computing system. A channel metadata repository is configured with channel configuration metadata for each channel of a plurality of channels available to the plurality of bots. The channel configuration metadata includes metadata that defines a specified format for communicating data with a target channel. A bot manager is configured to provide communication between each bot of the plurality of bots and each channel of the plurality of channels by at least converting output responses from each bot based on the channel configuration metadata associated with a target channel.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Systems and methods for evaluating reliability of a model are disclosed, including a processor that may include a data augmentor and a model evaluator. The data augmentor may receive a task data pertaining to information related to a pre-defined task to be performed by the model. The data augmentor may augment the task data to obtain an augmented aspect data. The model evaluator may evaluate a trained model based on the augmented aspect data to obtain aspect evaluation metrics. The model may be an artificial intelligence (AI) model that may be trained using the task data. The evaluation may enable to assess performance of the trained model by computing a performance score based on the aspect evaluation metrics. The performance score may help evaluate the reliability of the model in a pre-defined domain.

Abstract: Techniques for extracting data from conversations across different types of communication channels are disclosed. A system applies a set of rules to extract data from conversations based, at least in part, on a type of communication channel used for conducting the conversation. The system applies a machine learning model to recognize semantic content in conversations. The system divides conversations into conversation segments and classifies the conversation segments based on the semantic content. The system selects conversation segments to be extracted based on the semantic content and the type of communication channel over which a conversation is conducted. The system maps conversation segments from different conversations conducted on different types of communication channels to a same set of transactions.

Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, an ad request. The method also can include retrieving ad candidates from an ad database. The method further can include determining a respective ad ranking score for each of the ad candidates, based at least in part on the ad request and respective historical retrieval scores for each of the ad candidates.

Abstract: An access point that detects a duplicate address is described. During operation, the access point may receive a request associated with an electronic device, where the request includes an address of the electronic device (such as an IP address). In response, the access point may perform a comparison of the address to stored addresses of one or more electronic devices that are associated with the access point. When the comparison indicates that the address is in use by another electronic device in the one or more electronic devices, the access point may provide a response addressed to the electronic device that indicates that the address is in use. Otherwise, the access point may check with a remainder of a network to see if the address is in use. If the address is in use, the access point may provide the response.

Abstract: A traction battery venting system includes, among other things, an engine exhaust system that discharges engine exhaust gas to an area outside an electrified vehicle. The engine exhaust gas is emitted from an internal combustion engine of the electrified vehicle. The system further includes a sound deadening device of the engine exhaust system, a traction battery, and a battery vent system that communicates battery vent byproducts to the engine exhaust system at a position that is downstream from the sound deadening device. The battery vent byproducts are emitted from the traction battery of the electrified vehicle.

Abstract: A network device includes at least one port and a processor for use in a network for communicating a packet. The processor is configured to obtain a packet header for a packet and perform telemetry using postcard and/or passport approaches. The processor uses a repurposed field in the packet header to indicate telemetry is to be performed on the packet.

Abstract: Techniques for runtime customization and management of user interfaces for enterprise applications are disclosed. In some embodiments, the techniques include tools, systems, and processes for building frontend, technology-independent user interfaces. A system may consume user input files that are language dependent and generate a metadata file that is language independent. The metadata file may be updated to customize or otherwise modify a user interface of an application at runtime. An event signal may be raised to notify the application when a new user interface is available. In response to detecting the signal, the application may refresh the user interface, including the application page being currently viewed, to modify the set of user interface components that are rendered and displayed to the end user. The system may store metadata for multiple versions of the user interface to allow users to quickly roll back to prior versions of a user interface.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: Systems, methods and techniques for training and applying machine learning models to predict whether or not one or more individuals will suffer a fall event. In certain embodiments, a machine learning model can include both a static component and a dynamic component, where each component is associated with different types of medical data. In certain embodiments, an adjustment factor based on fall history of individuals is applied to the output of the machine learning model to generate a final score predictive of a fall event. In certain embodiments, the machine learning model is both trained and applied to medical data associated with predetermined forms, and where the predetermined forms include a value range associated with a medical condition.