Performing an operation comprising transforming an input dataset to a predefined format, extracting, from the transformed dataset, a plurality of features describing the transformed dataset, and generating, by a machine learning (ML) algorithm executing on a processor and based on an ML model, a plurality of rules for modifying the transformed dataset to conform with a first data model.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

The present disclosure provides an entity relation mining method based on a biomedical literature, including the following steps: querying a disease-associated biomedical literature in a public database, and performing data preprocessing to obtain biomedical text data; performing biomedical named entity recognition on obtained biomedical text data in combination with a regex matching pattern and a deep learning model; and mining an entity relation with transfer learning and reinforcement learning based on an entity recognition result. By acquiring the disease-associated biomedical literature from a network, extracting an abstract and a title and performing entity recognition and relation mining, the present disclosure can effectively recognize biomedical noun entities in the literature and mine potential relations between various entities.

The present disclosure provides an entity relation mining method based on a biomedical literature, including the following steps: querying a disease-associated biomedical literature in a public database, and performing data preprocessing to obtain biomedical text data; performing biomedical named entity recognition on obtained biomedical text data in combination with a regex matching pattern and a deep learning model; and mining an entity relation with transfer learning and reinforcement learning based on an entity recognition result. By acquiring the disease-associated biomedical literature from a network, extracting an abstract and a title and performing entity recognition and relation mining, the present disclosure can effectively recognize biomedical noun entities in the literature and mine potential relations between various entities.

Systems and methods for generating synthetic medical data are provided. A method may include retrieving a set of authentic electronic medical records from a database. The method may further include converting the authentic set of electronic medical records to a set of numerical vectors. The method may further include training a first neural network based on a random noise generator sample, the first neural network outputting synthetic electronic medical records. The method may further include training a second neural network based on the output synthetic electronic medical records and the set of numerical vectors, the second neural network outputting a loss distribution indicating whether the output synthetic electronic medical records are classified as authentic or synthetic.

A method includes generating an intervention model for a population of users based on contact data, demographic data, and engagement data indicating successfulness of prior interventions for each of the population of users. The method includes, obtaining first data related to a first user, including engagement data indicating successfulness of prior interventions with the first user. The method includes supplying the obtained data as input to the intervention model to determine an intervention expectation, which indicates a likelihood that the first user will take action in response to an intervention. The method includes determining a likelihood of a gap in care. The method includes, in response to the care gap likelihood exceeding a minimum threshold, selecting and scheduling execution of a first intervention. The first intervention is one of a real-time communication with the first user by a specialist and an automated transmission of a message to the first user.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.

A method and system are provided for estimating a physiological parameter using a parameter model determined by a deep neural network. An example method includes training a deep neural network with indirect and direct physiological parameters from a user database. The medical parameters include a respiratory rate, oxygen saturation, temperature, blood pressure, and pulse rate. The method includes determining if a new user belongs in a group. If the parameter model estimated physiological parameter using the closest group to the new user and associated calibration, then the method quantizes the parameter inputs to determine which physiological parameter a new user is most sensitive and to determine a new group and calibration coefficients or curves for the new user.

An information processing system includes a first information processing apparatus including a first inference unit configured to perform first inference processing on inference target medical data using a first partial model including an input layer and at least some of intermediate layers and corresponding to a plurality of second partial models, and a first output unit configured to output a result of the first inference processing and selection information to a second information processing apparatus, and the second information processing apparatus including a second inference unit configured to perform second inference processing by inputting a result of the first inference processing to a second partial model selected from among the plurality of second partial models based on the selection information.