Abstract: A system includes a database, a memory storing instructions, and a processor communicatively coupled to the memory and the database. The database stores a dataset comprising previous patient hospital stay data for a plurality of patients. The processor is configured to execute the instructions to generate training data based on the dataset, train a prediction model based on the training data, receive current patient hospital stay data for a current patient, generate a risk score of health deterioration for the current patient based on the prediction model and the current patient hospital stay data, and determine a likelihood of the current patient being transferred to an ICU within a selected period based on the risk score.

Abstract: A method includes retrieving social network connections of a user from a database and using the social network connections to assign a label to the user. The label indicates how the user will react to messages containing misinformation and messages containing refutations of misinformation. The label is assigned to the user without determining how the user has reacted to past messages containing misinformation.

Abstract: A method includes conserving processor resources by reducing a number of exercises presented to a student by applying a set of exercises performed by the student and the student's performance on those exercises to a neural network. A respective time period for each exercise in the set of exercises is applied to the neural network wherein each time period represents an amount of time since the student performed the exercise associated with the time period. For a candidate next exercise, a likelihood of the student successfully performing the candidate next exercise is obtained from the neural network and is used to select an exercise to present to the student next.

Abstract: A method can be implemented at one or more computing machines. The method can include receiving, using a server, time-series data corresponding to monitoring instrumentation in a medical care facility. The time-series data corresponds to a selected care recipient. The time-series data is stored in one or more data storage units. The time-series data includes data correlated with a plurality of regular time intervals. The method includes receiving, using a server, aperiodic data corresponding to clinical notes collected in the medical care facility and corresponding to the selected care recipient. The aperiodic data is stored in one or more data storage units. The aperiodic data includes a time stamp. The method includes generating, using a deep neural network and the time-series data and using a convolutional neural network (CNN) and the aperiodic data, a plurality of computer-generated data corresponding to management of the medical care facility or medical condition of the care recipient.

Abstract: A computing machine receives sensor data representing airflow or air pressure. The computing machine determines, using an artificial neural network, a current sleep stage corresponding to the sensor data. The current sleep stage is one of: wake, rapid eye movement (REM), light sleep, and deep sleep. The artificial neural network comprises a convolutional neural network (CNN), a recurrent neural network (RNN), and a conditional random field (CRF). The computing machine provides an output representing the current sleep stage.

Abstract: A method includes associating a person's activities up to a current time point with one of a plurality of clusters of activity proportions for the current time point and associating the person's activities up to the current time point with a sub-cluster in the cluster of activity proportions for the current time point. A determination is then made that the sub-cluster is associated with poor sleep and a recommendation of at least one activity to increase the likelihood of good sleep is made.

Abstract: A computing device may determine, based at least in part on one or more semantic exemplars associated with a digital object, a level of incongruity for the digital object, and output an indication of the level of incongruity for the digital object. For instance, a system may provide a web application that can support hedonic web surfing. By modeling Internet users as active information foragers instead of random surfers, a system may obtain quantitative measures of digital objects that users may find psychologically stimulating. The system may utilize a quantitative measure of the conceptual incongruity of digital objects that may predict how interesting users will find an object.

Abstract: Computerized systems for automating content annotation (e.g., tag creation and/or expansion) for low-content items within a computer network by leveraging intelligence of other data sources within a network to generate secondary content (e.g., a “context”) for items (e.g., documents) for use in a tagging process. For example, based on user assigned tags for an item, secondary content information can be generated and used to determine a new list of candidate tags for the item. Additionally, the context of an input item may be compared against the respective contexts of a plurality of other items to determine respective levels of similarity between the input item and each of the plurality of other items in order to annotate the input item. Techniques involving web-distance based clustering and leveraging crowd-sourced information sources to remove noisy data from annotated results are also described.

Abstract: Path variables of a mallet cube until a hit in an interactive game involving cubes movable in three dimensions are indicative of both hyperactivity and inattention.

Abstract: A method of diagnosing attention deficit hyperactivity disorder (ADHD). The method employs a tangible graphical interactive game wherein the interactive game employs a plurality of tangible graphical cubes. The game induces stimuli, measures responses and accumulates the responses using a predefined set of variables into a predefined set of metrics, wherein the variables are determined using an interactive machine learning feedback algorithm.

Abstract: Computerized systems for providing interest-to-item matching when item metadata is lacking or unavailable such that desired items of interest (e.g., research datasets) may be located for a user. For instance, the computing system may generate a context of a user's interest based on information indicating the user's interest (e.g., authors of research document, title of research document), and use the context to identify potentially relevant items and determine the relevance of the items to the user's interest. Additionally, a searchable database of items is generated by extracting identifiers of low content items from publicly available sources, such as the Internet, and generating contexts for the identified items. The computing system then indexes the identified items in the database using the generated contexts thereby enabling users to search the database for items of interest. Moreover, generating a context for items provides better accessibility for items that have little or no indexable content (e.g.

Abstract: Computerized systems for automating content annotation (e.g., tag creation and/or expansion) for low-content items within a computer network by leveraging intelligence of other data sources within a network to generate secondary content (e.g., a “context”) for items (e.g., documents) for use in a tagging process. For example, based on user assigned tags for an item, secondary content information can be generated and used to determine a new list of candidate tags for the item. Additionally, the context of an input item may be compared against the respective contexts of a plurality of other items to determine respective levels of similarity between the input item and each of the plurality of other items in order to annotate the input item. Techniques involving web-distance based clustering and leveraging crowd-sourced information sources to remove noisy data from annotated results are also described.

Abstract: Creation and various uses of an example model of preferences that displays certain types of time and history dependent dynamics are disclosed. Creation and use of the model may be based on insights from studies in human psychology and gained from the exploration of real world temporal preference data. Particularly, the dynamics of satiation for familiar content are incorporated in the model by dynamic item preference states. In some examples, the model may identify different latent preference states for items which are called the Sensitization, the Boredom, and the Recurrence states. Dynamics in a user's preferences for items may be attributed to the dynamics in these item states.

Abstract: Path variables of a mallet cube until a hit in an interactive game involving cubes movable in three dimensions are indicative of both hyperactivity and inattention.

Abstract: A computing device may determine, based at least in part on one or more semantic exemplars associated with a digital object, a level of incongruity for the digital object, and output an indication of the level of incongruity for the digital object. For instance, a system may provide a web application that can support hedonic web surfing. By modeling Internet users as active information foragers instead of random surfers, a system may obtain quantitative measures of digital objects that users may find psychologically stimulating. The system may utilize a quantitative measure of the conceptual incongruity of digital objects that may predict how interesting users will find an object.

Abstract: The social influence that each person in a computer network system exercises over others in the system may be valued by aggregating the differences in value of each of the others to the network both with and without the person being present. This calculated influence may be used as a basis for charging advertisers for advertisements to the users, as well as for providing preferential treatment to users that exert the greatest influence.

Abstract: A method of diagnosing attention deficit hyperactivity disorder (ADHD). The method employs a tangible graphical interactive game wherein the interactive game employs a plurality of tangible graphical cubes. The game induces stimuli, measures responses and accumulates the responses using a predefined set of variables into a predefined set of metrics, wherein the variables are determined using an interactive machine learning feedback algorithm.

Abstract: Gold farming refers to the illicit practice of gathering and selling virtual goods in online games for real money. Although around one million gold farmers engage in gold farming related activities, to date a systematic study of identifying gold farmers has not been done. Here data is used from the Massively Multiplayer Online Role Playing Game (MMOG) EverQuest II to identify gold farmers. This is posed as a binary classification problem and a set of features is identified for classification purposes. Given the cost associated with investigating gold farmers, criteria are also given for evaluating gold farming detection techniques, and suggestions provided for future testing and evaluation techniques.

Abstract: The social influence that each person in a computer network system exercises over others in the system may be valued by aggregating the differences in value of each of the others to the network both with and without the person being present. This calculated influence may be used as a basis for charging advertisers for advertisements to the users, as well as for providing preferential treatment to users that exert the greatest influence.

Abstract: A massively multiplayer online game (MMOG) monetization analysis computer system may include an electronic data processing system configured to electronically analyze data, including player behavioral logs, in accordance with one or more algorithms. Based on the analysis, the electronic data processing system may predict player changes that are relevant to monetization of the MMOG and generate a report relating to these predicted changes. The analysis may identify players whom are predicted to terminate their subscriptions to the MMOG, an amount of time each of several players is likely to spend playing the MMOG, and/or a number of transactions each of a several players is likely to engage in while playing the MMOG.