https://www.synthesis.clinic Our work is deeply linked to the area of ​​AI applied to healthcare, we developed an innovative telemedicine architecture in Graph Data Science, using a Neo4j graph database. The example of synthesis .clinic shows that in other areas the modeling of a complex system applied to AI must observe the flows of occurrences of people's event relationships with maps of companies' needs. Developing highly complex systems throughout our history, we believe we can contribute with Graph Data Science technology, creating solutions in this scenario at the beginning of the Artificial Intelligence era directly linked to people's daily lives. This Graph DB designed by new eco can also be used by your institution. The knowledge base developed includes all diseases mapped in the ICD (International Code of Diseases), containing 4 thousand symptoms. As well as all the necessary relationships between patients, symptoms, and diseases, information from more than a thousand scientific articles on the Pubmed platform and MeSH (Medical Subject Headings) was categorized to build the base. The model is available in the new eco repository on github. But if you prefer, get in touch [@health.eco.br] and we will be happy to present the diagnostic support model created by new eco for your institution and clinical staff. Through the systhesis.clinic web console we can provide direct access to the Graph DB without the need to carry out the more technical import process, since when you are not familiar with the universe of graph data science, more specifically the database in neo4j graph, it may seem like a complex process. Therefore, we are available to facilitate the process of visualizing the developed model in operation. synthesis .clinic model data: 4 thousand Symptoms; 22 thousand Disease Terms; 13 thousand anonymized patients; 16 thousand Patterns (/Groups) recognized by GDS algorithms; 433 Clusters of Related Diseases Recognized by GDS Algorithms; 1.5 thousand Symptom Attribution Events attributed to patient X; 4.7 thousand Terms of Symptoms Associated with ICD Diseases; 7.9 thousand PubMed and MeSH Terms Associated with the Proposed GDS Model; 7.3 thousand Disease Terms related to ICD classes; 5.3 thousand Diagnostic relationships associating Patients with Diseases; 98 thousand relationships between Symptoms and Diseases; 103 thousand associations of symptoms related to Diseases; 4.2 thousand Source Symptoms to Target Grouped Symptoms relationship; 53 thousand Disease Relationships Grouped for Diseases; 25 thousand Diseases that make up their relationships; 7.9 thousand lists of Grouped Symptoms for Grouped diseases; 25 thousand grouped disease lists for Other Grouped Diseases; 76 thousand Diseases for Grouped Diseases; 26 thousand Symptom relationships for grouped Disease relationships; 2 thousand symptom relationships grouped for disease relationships; 3.3 thousand Grouped symptom relationships for Grouped Disease relationships; 1 thousand disease terms associated with ICD subgroups.

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.

https://www.synthesis.clinic Through our experience, we can model your Diagnostic knowledge base to be applied to your patients. The main concepts used in the design of the Knowledge Synthesis project are based on the new generation of super apps expected in the coming years, which now aim exclusively at building knowledge bases. The basic idea is to concentrate technological efforts to provide a personalized and customized digital environment, allowing the centralization and storage of all information securely on a high-performance platform. The computational model proposed by new eco for the project is entirely based on the universe of Knowledge Management and focuses on building personalized bases in Graph Data Science using multidisciplinary concepts from computing and neuroscience.