Abstract: A diagnosis method, system, and computer program product, include processing a user input comprising medical symptoms, assigning the user to a team of physicians and a cognitive agent based on the processed medical symptoms, each of the physicians having a different medical specialty corresponding to a disease associated with the medical symptoms, and producing a consensus diagnosis from the team of physicians and the cognitive agent.

Abstract: A sentiment analyzer obtains natural language media input and determines sentiment of the natural language media input. A personality analyzer obtains data indicative of a personality of an originator of the natural language media input and determines a personality metric of the originator of the natural language media input. The sentiment of the natural language media input and the personality metric of the originator of the natural language media input are combined to obtain a personality-based sentiment of the natural language media input. The natural language media input is provided to a receiver together with a representation of the personality-based sentiment of the natural language media input.

Abstract: A sentiment analyzer obtains natural language media input and determines sentiment of the natural language media input. A personality analyzer obtains data indicative of a personality of an originator of the natural language media input and determines a personality metric of the originator of the natural language media input. The sentiment of the natural language media input and the personality metric of the originator of the natural language media input are combined to obtain a personality-based sentiment of the natural language media input. The natural language media input is provided to a receiver together with a representation of the personality-based sentiment of the natural language media input.

Abstract: A sentiment analyzer obtains natural language media input and determines sentiment of the natural language media input. A personality analyzer obtains data indicative of a personality of an originator of the natural language media input and determines a personality metric of the originator of the natural language media input. The sentiment of the natural language media input and the personality metric of the originator of the natural language media input are combined to obtain a personality-based sentiment of the natural language media input. The natural language media input is provided to a receiver together with a representation of the personality-based sentiment of the natural language media input.

Abstract: A diagnosis method, system, and computer program product, include processing a user input comprising medical symptoms, assigning the user to a team of physicians and a cognitive agent based on the processed medical symptoms, each of the physicians having a different medical specialty corresponding to a disease associated with the medical symptoms, and producing a consensus diagnosis from the team of physicians and the cognitive agent.

Abstract: Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition. The machine learning model may predict one or more of the health care providers that provide treatment for the user's health condition that matches the user's preference. The machine learning model may be retrained based on one or more of feedback from the user, the health care providers, and updated traits of the users and the health care providers.

Abstract: A sentiment analyzer obtains natural language media input and determines sentiment of the natural language media input. A personality analyzer obtains data indicative of a personality of an originator of the natural language media input and determines a personality metric of the originator of the natural language media input. The sentiment of the natural language media input and the personality metric of the originator of the natural language media input are combined to obtain a personality-based sentiment of the natural language media input. The natural language media input is provided to a receiver together with a representation of the personality-based sentiment of the natural language media input.

Abstract: Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition. The machine learning model may predict one or more of the health care providers that provide treatment for the user's health condition that matches the user's preference. The machine learning model may be retrained based on one or more of feedback from the user, the health care providers, and updated traits of the users and the health care providers.

Abstract: Training a machine to provide specialized health care apparatus may include receiving text describing a user's health condition via a user interface. Text may be converted into corresponding medical terms. A database may be searched for a list of health care providers treating health conditions associated with the medical terms. A machine learning model may be built that may include user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition. The machine learning model may predict one or more of the health care providers that provide treatment for the user's health condition that matches the user's preference. The machine learning model may be retrained based on one or more of feedback from the user, the health care providers, and updated traits of the users and the health care providers.