Improvement in Analyzing Healthcare Systems using Deep learning Architecture

The Complexity of analyzing the healthcare systems demands in many inference key challenges. The real time applications of knowledge management and causal mapping relations of health care data research require a distributed deep learning architecture. This paper presents an application oriented service share model for the generalizations of knowledge processing and mapping for integrating healthcare systems. Based on the model, a distributed deep learning architecture is presented that can be readily integrate various healthcare database and systems transparently. This architecture provides a standard interface for loading various healthcare modules and that helps in processing the knowledge, which in turn can be used as input for other healthcare systems decision making. The required inputs are given in the form of Natural Language Processing (NLP) sentences which in turn converted to queries. Domain Ontologies contains several lexicons or medical terminologies belonging to that domain. They will have an image to identify the concepts and relations between concepts that emerge. This methodology is based on producing a supervised clustering which highlights semantically relevant concepts and relations which will be linked using causal relations.