A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

Aspects described herein describe providing content recommendations such as, for example, recommendations for video content. A content recommendation may be based on when content was previously consumed.

A method for dynamically managing a goal in a care plan of a patient is disclosed. The method includes receiving a selection of a type of the care plan for the patient, responsive to the selection of the type of the care plan, receiving a selection of a goal having a goal type to include in the care plan, generating the care plan including the goal having the goal type, and causing the care plan including the goal to be presented on a computing device of a medical personnel.

This disclosure relates to recommendations made to users based on learned behavior patterns. User behavior data is collected and grouped according labels. The grouped user behavior data is labeled and used to train a machine learning model based on features and tasks associated with the classification. User behavior is then predicted by applying the trained machine learning model to the collected user behavior data, and a task is recommended to the user.

A method of training a module in an AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes at least an AI module executing a model, a dataset, and the module adapted to be trained. The method includes receiving input data in the module adapted to be trained, labelling data as good data and bad data in the module adapted to be trained, classifying binarily the labelled good data and the labelled bad data in the module adapted to be trained, inputting the binarily classified data into the AI module, and recording internal behavior of the AI module in response to the binarily classified data on the module adapted to be trained.

A method for autonomously generating a care plan personalized for a patient is disclosed. The method includes receiving a selection of a type of the care plan to implement for the patient, generating the care plan based on the type selected, wherein the care plan includes an action instruction based on a patient graph of the patient and a knowledge graph including ontological medical data, receiving patient data that indicates health related information associated with the patient, modifying the care plan to generate a modified care plan in real-time or near real-time based on the patient data, and causing the modified care plan to be presented on a computing device of a medical personnel.

An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.

Some embodiments of the present application include obtaining datasets including a plurality of features and computing a correlation score between each of the features. Based on the correlation scores, the features may be clustered together such that each cluster includes features that are correlated with one another, and features included in different feature clusters lack correlation with one another. A machine learning model may be selected based on a set of input features for the model and the plurality of clusters such that each input feature is included in one of the feature clusters and no feature cluster includes more than one of the input features. Datasets may then be selected based on the set of input features, which may be used to generate training data for training the machine learning model.

A method for analyzing a specification parameter of an electronic component includes inputting a package type and at least one engineering drawing image of an electronic component; acquiring a probability value that in each view of the different viewing directions each of the plurality of specification parameter of the electronic component is labeled; taking the view of each of the plurality of specification parameters in the view direction with a highest probability value as a recommended view; performing a box selection on the plurality of specification parameters for at least one engineering drawing image with the same viewing direction as that of the recommended view by an object detection model; and identifying box-selected specification parameters to acquire a size value of identified specification parameters from the at least one engineering drawing image, and converting the size value into a corresponding editable text for output.

A method of determining whether a user has fallen comprises detecting a potential fall using a motion sensing device, updating a probability of the potential fall being an actual fall based on an additional sensor, and updating the probability of the potential fall being an actual fall based on user context, the user context including an identified activity prior to the potential fall.