Abstract: The present disclosure describes methods and systems for machine learning models utilized for diagnosis suspecting. These methods and systems utilize machine learning models may be trained to diagnose diseases or conditions. The models may be trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

Abstract: The present disclosure describes methods and systems for machine learning models utilized for identifying issues with medications and gaps in care. The present disclosure also describes methods and systems for machine learning models utilized to provide recommended actions for addressing the issues with medication and gaps in care. These methods and systems utilize machine learning models that are trained to identify issues with medications and gaps in care, as well as provide recommended actions for those medications issues and gaps in care. The models are trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

Abstract: Systems and methods including analyzing profiles, generating medical recommendations, prioritizing the medical recommendations, throttling the medical recommendations, and transmitting a prioritized set of medical recommendations to a device that displays the information are disclosed. The medical recommendations may be prioritized based on an importance of the recommendation to a patient, a medical severity, and/or prior interaction with the system. The medical recommendations may be throttled to determine a number of recommendations to display to a medical provider during a period of time, such as a patient examination.

Abstract: Systems and methods including analyzing profiles, generating medical recommendations, prioritizing the medical recommendations, throttling the medical recommendations, and transmitting a prioritized set of medical recommendations to a device that displays the information are disclosed. The medical recommendations may be prioritized based on an importance of the recommendation to a patient, a medical severity, and/or prior interaction with the system. The medical recommendations may be throttled to determine a number of recommendations to display to a medical provider during a period of time, such as a patient examination.

Abstract: The present disclosure describes methods and systems for machine learning models utilized for diagnosis suspecting. These methods and systems utilize machine learning models may be trained to diagnose diseases or conditions. The models may be trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

Abstract: Systems and methods including analyzing profiles, generating recommendation(s) and supporting evidence associated with the recommendation(s) related to medical services provided to a patient, and transmitting the recommendation(s) and supporting evidence associated with the recommendation(s) to a device that displays the information are disclosed. The supporting evidence may be presented based on a statistical relevance of the information and/or a likelihood that a medical professional will utilize the information.

Abstract: Systems and methods including analyzing profiles, generating questions relate to suspected diagnoses of a patient, and transmitting the questions to a device that displays the questions are disclosed. Responses to the questions are received which may be used in subsequent suspected diagnosis, generating updated questions, or confirming a suspected diagnosis.