Scientific clinical diffusion network

A scientific clinical diffusion system comprising:

• a computing device comprising at least one processor;
• and
• a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• generating, from clinical data associated with a clinical network of healthcare providers, a first graph network representing the clinical network, the first graph network comprising first nodes representing healthcare providers and first edges connecting two first nodes and indicative of influence between the corresponding healthcare providers of the connected first nodes;
• generating, from scientific data associated with a scientific network of scientists and using a machine learning engine, a second graph network representing the scientific network and indicative of influence between different scientists of the scientists, the second graph network comprising second nodes and second edges, each second node representing a corresponding scientist from the scientists, each second edge connecting two second nodes and indicative of influence between the corresponding scientists of the connected second nodes;
• identifying, using survey data representing healthcare sociometric information of the clinical network and scientific network, a first set of target nodes in the first graph network and a second set of target nodes in the second graph network, wherein each node in the first set of target nodes represents a clinical leader in the clinical network, and wherein each node in the second set of target nodes represents a scientific leader in the scientific network;
• predicting, using the first and second set of target nodes and by a link prediction model, links connecting the first graph network to the second graph network;
• generating a heterogenous network comprising third nodes and third edges, wherein the third nodes comprise the first nodes from the first graph network and the second nodes from the second graph network, and wherein the third edges comprise the links connecting the first graph network and the second graph network;
• determining, from the heterogenous network, a first time instance associated with scientific evidence, wherein the first time instance represents a time instance of a scientific publication being associated with at least a subset of the third nodes;
• measuring, for each of one or more additional time instances of the heterogenous network and using the links of the heterogenous network associated with the first set of target nodes, an adoption volume of the scientific evidence for the corresponding healthcare providers from the first set of target nodes;
• and
• determining, from the measured adoption volume, a diffusion weight of the scientific evidence for a link from the links connecting the first graph network to the second graph network.

A non-transitory computer-readable storage device storing instructions that when executed by one or more processors of a computing device cause the one or more processors to perform operations comprising:

• generating, from clinical data associated with a clinical network of healthcare providers, a first graph network representing the clinical network, the first graph network comprising first nodes representing healthcare providers and first edges connecting two first nodes and indicative of influence between the corresponding healthcare providers of the connected first nodes;
• generating, from scientific data associated with a scientific network of scientists and using a machine learning engine, a second graph network representing the scientific network and indicative of influence between different scientists of the scientists, the second graph network comprising second nodes and second edges, each second node representing a corresponding scientist from the scientists, each second edge connecting two second nodes and indicative of influence between the corresponding scientists of the connected second nodes;
• identifying, using survey data representing healthcare sociometric information of the clinical network and scientific network, a first set of target nodes in the first graph network and a second set of target nodes in the second graph network, wherein each node in the first set of target nodes represents a clinical leader in the clinical network, and wherein each node in the second set of target nodes represents a scientific leader in the scientific network;
• predicting, using the first and second set of target nodes and by a link prediction model, links connecting the first graph network to the second graph network;
• generating a heterogenous network comprising third nodes and third edges, wherein the third nodes comprise the first nodes from the first graph network and the second nodes from the second graph network, and wherein the third edges comprise the links connecting the first graph network and the second graph network;
• determining, from the heterogenous network, a first time instance associated with scientific evidence, wherein the first time instance represents a time instance of a scientific publication being associated with at least a subset of the third nodes;
• measuring, for each of one or more additional time instances of the heterogenous network and using the links of the heterogenous network associated with the first set of target nodes, an adoption volume of the scientific evidence for the corresponding healthcare providers from the first set of target nodes;
• and
• determining, from the measured adoption volume, a diffusion weight of the scientific evidence for a link from the links connecting the first graph network to the second graph network.