Distributed Retrieval of Drug Information Based on Deep Learning Driven by Big Data
As the composition of drug information itself is relatively complex,it is difficult to guarantee the relevance of search results.Therefore,a research on distributed retrieval of drug information driven by big data based on deep learning was proposed.A neural network discrim-inator for matching drug information features was designed to carry out drug information matc-hing analysis,and cross entropy loss function was introduced to calculate the true and false of the binary classification problem(judging whether two drugs match).In the distributed traversal retrieval stage,a unique identifier is assigned to the node,and the corresponding access status is set as unvisited."Maximum number of traversal nodes"is taken as a variable to dynamically update the traversal process of retrieval.Under the principle of FIFO,when all nodes have been accessed and no new drug information is retrieved,the traversal process ends.Output TOP-N drug information queue with the highest fit to the target retrieval information.In the test re-sults,the correlation of the search results is always above 0.90,and the maximum value is 0.947.
Deep learningDrug informationDistributed searchFeature matchingNeural net-work discriminatorCross entropy loss functionFirst in first out principle
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