synthesis technology e1

https://technology.synthesis.eco.br This is an innovative telemedicine synthesis architecture applied to Graph Data Science using the Neo4j Graph DBs bank. We were able to put all the classified diseases in the world and the main symptoms from PubMed into it. Graph DB has fifty thousand terms for Patients, Symptoms, and Diseases; and four hundred thousand relationships between us. According to the opinion of the main experts: It would not be possible to build the model with these characteristics in a relational database. In this Complex System, it is possible to show the viability of the idea of ​​proposing an intelligence storage solution based on the construction of personal patient databases. In the near future, one person will have a system larger than that of a large company today. Considering models as dependent data. The proposal was to create an architecture to significantly reduce the volume of knowledge base relationships. To resolve the issue of isolation, the concept of knowledge synthesis bases was created, which gives the project its name. Zero-knowledge bases receive patient data and diagnostic maps so that intelligent agents validate the most likely hypotheses. At this point, all AI is already pre-stored in tags and relationships within the synthesis base, with their respective values ​​pre-calculated by AI algorithms that have been validated by medical experts. This is where the concept of Knowledge Place comes in to make the solution profitable. Patients do not pay for the app, but rather for the knowledge applied to their health needs. Because, as we know, the analysis of a patient's diagnosis can have different levels of abstraction for different medical specialists! One of the possibilities is through this innovative architecture to build a super app within synthesis.eco.br in React/Next on the Vercel platform to create dynamic forms and navigate between fifty thousand terms with a maximum of fifty choices, or assignments, made through questions with guidelines or suggested answers. Thus allowing navigation in the Graph DB in an intelligent way. The interaction between patients and medical specialists to define a diagnostic hypothesis can be synchronous or asynchronous. The magic happens with the identification of the initial context, that is, all patients who are approximately the same as the analyzed patient who already has a diagnosis assigned in the knowledge base. By entering this group, we could check the complementary concepts of patients who have already been diagnosed, taking the shortest path within the graph to reach the objective. Smarter queries that find only the classificatory terms can be used at this point. Finishing the process, we show the terms, or classificatory symptoms, for the target diagnoses.