Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a heterogenous network and determining diffusion weights of scientific evidence in the heterogenous network. The computer obtains clinical data associated with a clinical network of healthcare providers and scientific data associated with a scientific network of scientists. The computer generates a first graph network representing the clinical network and a second graph network representing the scientific network. The computer identifies a first set of target nodes in the first graph network representing clinical leaders, and a second set of target nodes in the second graph network representing scientific leaders. The computer predicts links connecting the first graph network to the second graph network and generates a heterogenous network with nodes from the first graph network and from the second graph network, and the links connecting the first graph network to the second graph network.

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: 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.