clinic e2

https://www.synthesis.clinic Appropriate modeling was carried out on the contents of the instances of each mapped element. Groups of symptoms and diseases emerged. These are differentiating elements in the proposed model, evolving the basic idea that physiology is directly related to symptoms and not just diseases, a fact that has long been proven by studies with high scientific impact. Thus, the technological innovation of the presented model lies in structuring a unique platform to support AI and Temporality. Tags are created from prior processing. The schema maps approximately 50 thousand concepts distributed across the elements of the model diagram. With more than half a million relationships, observing the nature of instance-based modeling, it is defined that a system of this size in the healthcare area must be understood as complex. Therefore, the knowledge engineer can't predict all of its possibilities, just from its modeling. Simulation techniques and modeling of complex systems are necessary for their definition and development. Due to the complexity of this topic, it may be useful to see the series: simple.mind.eco.br, thinking.mind.eco.br and complex.brain.echo.br. Likewise, we are available to present the basis of the synthesis.clinic for any doubts and/or make any necessary clarifications. The Graph DB schema diagram of the proposed healthcare knowledge synthesis model shows that each edge is assigned to a significant set of instances of diseases and symptoms, the "Groups" elements are part of the prior organization. The "Grouped" elements were generated from the processing of GDS (Graph Data Science) algorithms. Attention is drawn to the fact that the base elements form a triangle between "Patients", "Symptoms" and "Diseases". Groups and groupings, therefore, are satellite elements of this triad. In this sense, synchronization is established between the organization of reasoning of specialist doctors and Artificial Intelligence algorithms.