Abstract: The present disclosure provides methods for accurately predicting the dynamics of symptom response to drugs or other interventions for the treatment of major depressive disorder or other psychological conditions. These methods can allow for a shortening of the time period necessary for the evaluation of a drug or other therapeutic intervention. These predictive methods are based on measured and/or self-reported symptom severity measures at two or more points in time. These measures are then discretized into symptom classes (e.g., low, moderate, severe) and the symptom classes are then applied to the predictive model to predict the progression of symptoms and/or the effectiveness of a drug or other therapeutic intervention.

Abstract: The disclosed system and method provide a way to easily review, audit, and modify multiple users' security access authority. The disclosed system and method may include using a trained bot that is trained using a machine learning algorithm to retrieve user profile information from a complex network of tables stored in a relational database and to analyze the user profile information to generate a recommendation to revoke or approve at least one user's security access authority for at least one transaction type. In some embodiments, the disclosed system and method allow for a human expert to revoke or approve at least one user's security access authority for at least one transaction type based on the recommendation. In some embodiments, the disclosed system and method determine whether to revoke or approve at least one user's security access authority for at least one transaction type and may also revoke or approve the at least one user's security access authority based on the decision.

Abstract: A security monitoring platform may use an unsupervised machine learning technique to cluster historical data related to user access rights associated with multiple cloud applications based on various features that relate to user permissions and attributes within the multiple cloud applications. The security monitoring platform may use a supervised machine learning technique to train an access rights data model based on the clustered historical data and perform one or more actions that relate to current access rights assigned to at least one user within one or more of the multiple cloud applications based on a score representing a probability that an access level assigned to the at least one user within the one or more of the multiple cloud applications is correct. The security monitoring platform may apply a reinforcement learning technique to update the access rights data model based on feedback related to the one or more actions.

Abstract: The disclosed system and method provide a way to easily review, audit, and modify multiple users' security access authority. The disclosed system and method may include using a trained bot that is trained using a machine learning algorithm to retrieve user profile information from a complex network of tables stored in a relational database and to analyze the user profile information to generate a recommendation to revoke or approve at least one user's security access authority for at least one transaction type. In some embodiments, the disclosed system and method allow for a human expert to revoke or approve at least one user's security access authority for at least one transaction type based on the recommendation. In some embodiments, the disclosed system and method determine whether to revoke or approve at least one user's security access authority for at least one transaction type and may also revoke or approve the at least one user's security access authority based on the decision.

Abstract: A security monitoring platform may use an unsupervised machine learning technique to cluster historical data related to user access rights associated with multiple cloud applications based on various features that relate to user permissions and attributes within the multiple cloud applications. The security monitoring platform may use a supervised machine learning technique to train an access rights data model based on the clustered historical data and perform one or more actions that relate to current access rights assigned to at least one user within one or more of the multiple cloud applications based on a score representing a probability that an access level assigned to the at least one user within the one or more of the multiple cloud applications is correct. The security monitoring platform may apply a reinforcement learning technique to update the access rights data model based on feedback related to the one or more actions.

Abstract: This specification discloses systems, methods, devices, and other techniques for determining the location of a seizure-generating region of the brain of a mammal.