Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, each de-identified longitudinal medical record representing a record of a different anonymized patient and encoding information identifying a treatment received by the anonymized patient and receiving notification data including notification records, each notification record encoding information identifying a channel through which the notification was provided. The method includes determining a first channel impact model representing an impact of a notification provided through a first channel on a treatment being received, a second channel impact model representing an impact of a notification provided through a second channel on a treatment being received, and determining a multi-channel impact model representing an impact of notifications being provided through both the first channel and the second channel on a treatment being received.

Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: Methods and systems to identify collaborative communities of individuals from graphs of multiple types of relationships amongst the individuals, including to mine data related to multiple types of relationships amongst individuals, construct graphs to represent the respective types of relationships amongst individuals, and perform a multiplex graph convolutional network (MGCN) artificial intelligence machine learning (AIML) analysis across the multiple graphs to identify the collaborative communities. A mathematical representation of the graphs may be learned/tuned to optimize clustering of the individuals. Multiple parameters (inter-graph weights, consensus regularization function) may be jointly tuned based on a joint optimization function. The collaborative communities may be displayed such that relative positions of the individuals represent measures of influence exerted by the respective individuals within the respective collaborative communities.

Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: Systems and techniques are disclosed for using machine-learning to identify potential opportunity patients that are more likely to adjust his/her preference for a healthcare provider or service. In some implementations, integrated patient data is obtained. A patient sequence feature vector, a provider sequence feature vector, and a set of entity-specific feature vectors are generated. A set of opportunity patients is identified. A notification is transmitted to the set of opportunity patients about a second treatment plan.

Abstract: Techniques are disclosed for fleet vehicle management. According to various embodiments, a status is determined for each fleet vehicle of a plurality of fleet vehicles. Based on the status, a service task is determined for at least a subset of the plurality of fleet vehicles. A location is determined for each service task. A service task list is generated based on the service tasks and the locations of the service tasks. A user to perform the service task list is determined based on the service tasks and the service task locations. The service task is communicated to the user and progress of the service task list is monitored.

Abstract: A computer-assisted method to provide timely multi-channel notification of treatments to healthcare providers and patients, the method including receiving de-identified longitudinal medical records, treatment prescription records of healthcare providers, and notification data. Relationships between the healthcare providers, the anonymized patients, and the notifications are identified using the de-identified longitudinal medical records, the treatment prescription records of the healthcare providers, and the notification data. An impact of notifications being received by both the healthcare provider for the anonymized patient and the anonymized patient on whether the anonymized patient received the treatment is determined.

Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, receiving notification data, identifying anonymized patients that received the treatment, identifying notifications for the treatment that were received by the recipients, determining, for each of the identified notifications, whether the recipient is an anonymized patient identified as having received the treatment, determining, for each of the identified notifications for the treatment determined to be received by a recipient that is an anonymized patient identified as having received the treatment, a time relationship between the time when the treatment was received by the anonymized patient and the time that the notification was received by the recipient that is the anonymized patient, and determining, for each of the anonymized patients that received the treatment, associations between one or more time relationships for notifications received by the anonymiz

Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, each de-identified longitudinal medical record representing a record of a different anonymized patient and encoding information identifying a treatment received by the anonymized patient and receiving notification data including notification records, each notification record encoding information identifying a channel through which the notification was provided. The method includes determining a first channel impact model representing an impact of a notification provided through a first channel on a treatment being received, a second channel impact model representing an impact of a notification provided through a second channel on a treatment being received, and determining a multi-channel impact model representing an impact of notifications being provided through both the first channel and the second channel on a treatment being received.

Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, receiving notification data, identifying anonymized patients that received the treatment, identifying notifications for the treatment that were received by the recipients, determining, for each of the identified notifications, whether the recipient is an anonymized patient identified as having received the treatment, determining, for each of the identified notifications for the treatment determined to be received by a recipient that is an anonymized patient identified as having received the treatment, a time relationship between the time when the treatment was received by the anonymized patient and the time that the notification was received by the recipient that is the anonymized patient, and determining, for each of the anonymized patients that received the treatment, associations between one or more time relationships for notifications received by the anonymiz