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.

https://saude.sintese.de Nosso trabalho está profundamente ligado à área da ia aplicada à saúde, desenvolvemos uma arquitetura inovadora de telemedicina em Graph Data Science, utilizando banco de dados em grafo Neo4j. O exemplo do saude.sintese.de, mostra que em outras áreas a modelagem de um sistema complexo aplicado à ia, deve observar os fluxos das ocorrências dos relacionamentos dos eventos das pessoas com mapas das necessidades das empresas. Desenvolvendo sistemas de alta complexidade, durante toda nossa trajetória, acreditamos poder contribuir com tecnologia de Graph Data Science, criando soluções nesse cenário de início da era da Inteligência Artificial diretamente ligada à vida cotidiana das pessoas. Este Graph DB projetado pela new eco também pode ser utilizado pela sua instituição. A base de conhecimento desenvolvida contempla todas as doenças mapeadas no CID (Código Internacional de Doenças), contendo 4 mil sintomas. Assim como, todas as devidas relações entre pacientes, sintomas e doenças, para a construção da base foram categorizadas informações de mais de mil artigos científicos da plataforma Pubmed e do MeSH (Medical Subject Headings). O modelo está disponível no repositório da new eco no github. Mas caso prefira, entre em contato [@health.eco.br] e teremos o maior prazer em apresentar o modelo de apoio a diagnóstico criado pela new eco para sua instituíção e seu corpo clínico. Através do console web saude.sintese.de podemos fornecer acesso direto ao Graph DB sem a necessidade de realizar o processo mais técnico de importação, já que quando não se esta familiarizado com o universo do graph data science, mais específicamente o banco de dados em grafo neo4j, pode parecer um processo complexo. Assim, ficamos a disposição para facilitar o processo de visualização do modelo desenvolvido em funcionamento. Dados do modelo saude.sintese.de: 4 mil Sintomas; 22 mil Termos de Doenças; 13 mil Pacientes anonimizados; 16 mil Padrões(/Agrupamentos) reconhecidos pelos algoritmos da GDS; 433 Agrupamentos de Doenças Relacionadas Reconhecidas pelos Algoritmos da GDS; 1,5 mil Eventos de Atribuição de Sintomas atribuídos ao paciente X; 4,7 mil Termos de Sintomas Associados às Doenças do CID; 7,9 mil Termos do PUBmed e MeSH Associados ao Modelo de GDS Proposto; 7,3 mil Termos de Doenças relacionados às classes do CID; 5,3 mil relacionamentos Diagnósticos associando Pacientes às Doenças; 98 mil relacionamentos entre Sintomas e Doenças; 103 mil associações de sintomas relacionados a Doenças; 4,2 Sintomas de origem para relação Sintomas Agrupados de Destino; 53 mil Relações de Doenças Agrupadas para as Doenças; 25 mil Doenças que compõem as suas relações; 7,9 mil relações de Sintomas agrupados para doenças Agrupadas; 25 mil relações de doenças Agrupadas para Outras Doenças Agrupadas; 76 mil Doenças para Doenças agrupadas; 26 mil Relacionamentos de sintomas para as relações de Doenças agrupadas; 2 mil relações de sintomas agrupados para as relações de doenças; 3,3 mil Relações de sintomas agrupados para as relações de Doenças Agrupadas; 1 mil termos de doenças associados aos subgrupos do CID.

https://saude.sintese.de Realizadas as devidas modelagens nos conteúdos das instâncias de cada elemento mapeado. Surgiram os agrupamentos de sintomas e doenças. Estes são elementos diferenciais no modelo proposto, evoluindo as ideias básicas de que a fisiologia está diretamente relacionada aos sintomas e não apenas às doenças, fato já há muito tempo comprovado por estudos de alto impacto científico. Assim, a inovação tecnológica do modelo apresentado está na estruturação de uma plataforma única para suporte a IA e a Temporalidade. Sendo as tags criadas a partir de processamento prévio. O schema mapeia aproximadamente 50 mil conceitos distribuídos nos elementos do diagrama do modelo. Com mais de meio milhão de relacionamentos, observando a natureza da modelagem baseada nas instâncias, define-se que um sistema desse porte na área da saúde deve ser entendido como complexo. Não sendo possível, desta forma, para o engenheiro do conhecimento prever todas as possibilidades deste, apenas a partir da sua modelagem. Fazendo-se necessárias técnicas de simulação e modelagem de sistemas complexos para sua definição e desenvolvimento. Em virtude da complexidade desse tema, pode ser útil ver as séries: simples.mind.eco.br, pensamento.mind.eco.br e complexo.brain.eco.br. Da mesma forma, ficamos à disposição para apresentar a base do saude.sintese.de para e ventuais dúvidas e ou fazer os devidos esclarecimentos que se façam necessários. O diagrama do schema do Graph DB do modelo proposto de síntese do conhecimento na área da saúde mostra que cada aresta é atribuída a um conjunto significativo de instâncias das doenças e sintomas, os elementos "Grupos" são parte da organização prévia. Os elementos "Agrupados" foram gerados a partir dos processamentos dos algoritmos de GDS (Graph Data Science). Chama-se a atenção para o fato de que os elementos base formam um triângulo entre "Pacientes", "Sintomas" e "Doenças". Os Grupos e agrupamentos, portanto, são elementos satélites dessa tríade. Neste sentido, estabelece-se um sincronismo entre a organização do raciocínio dos médicos especialistas aos algoritmos de Inteligência Artificial.

https://saude.sintese.de Através da nossa experiência podemos modelar sua base de conhecimento Diagnóstico para ser aplicada aos seus pacientes. Os principais conceitos utilizados na concepção do projeto Síntese de Conhecimento baseiam-se na nova geração de super apps esperada para os próximos anos, que agora visam exclusivamente a construção de bases de conhecimento. A ideia básica é concentrar esforços tecnológicos para proporcionar um ambiente digital personalizado e customizado, permitindo a centralização e armazenamento de todas as informações de forma segura em uma plataforma de alto desempenho. O modelo computacional proposto pela new eco para o projeto é inteiramente baseado no universo da Gestão do Conhecimento e tem como foco a construção de bases personalizadas em Graph Data Science utilizando conceitos multidisciplinares das áreas de computação e neurociência.

doctor.health.eco.br Everything is recurrent, the cycles close in different units of time: seconds, for the facial response in a conversation; minutes to the next breath, hours to sleep; days to eat; years to learn; and a life to live. The complexity of the system cannot be measured, but the fact is that there is no possibility of providing quality medical diagnostic support with PATIENT data lost in institutional databases, where they are just a record of a set of tables and relationships. The Future of AI lies in edge prediction: BiTemporality is an important concept, we always need to know the current state of the patient, this is the FIRST DIMENSION; We also need to know what his clinical condition was like previously, so the SECOND DIMENSION is a step back in time. In this sense, with the time machine at our disposal, we can carry out cross-sectional analyses to identify the evolution of a certain behavior or physical symptom. Due to the large volume of information and the high flow of data, more complex algorithms are restricted to being applied previously at the time of clinical analysis. For this reason, it is possible to use the same temporal storage structure to place the processing results of these algorithms in tags directly in knowledge bases, identifying behaviors from previous clinical cases. For example, we create a node to relate two or three symptoms (cluster) and link them directly to the disease with the respective probability. Therefore, if a patient presents these symptoms, we can carry out a complimentary consultation about the symptoms that would lead to a certain diagnostic hypothesis. With these intelligent filters, it is possible to create dynamic forms to browse 50 concepts within a universe of 50 thousand, with at least 500 thousand relationships. Even for great specialists, it would not be possible to verify all of these possibilities due to the significant increase in technologies that generate more and more clinical information. It is no longer about knowledge or intelligence, the limit becomes the processing capacity of the human brain. Another aspect is that different situations generate different needs. Therefore, the more specific the diagnosis, the further it will be from the most common symptoms. There is a limit to how much a patient knows about their situation, they can know about their fatigue. But he hasn't studied all his life to know that there is a congenital problem in his left ventricle without the support of a specialist. In this way, the diagnosis migrates some concepts from the PATIENT's knowledge base to the specialist's distinct universe with over 50 thousand concepts. Now imagine being called in for an appointment because a specialist didn't like the results of some of your tests. Here we can leave the universe of diagnosed disease and enter the LOOP of preventive health.

doctor.health.eco.br All PATIENT data is assigned by doctors who see multiple other patients in different clinics and hospitals. But shouldn't a person's clinical data be in your possession? On the other hand, different behaviors cause symptoms and illnesses over time. Likewise, care over time generates health benefits. Different experiences generate different levels of complexity for each person. Even things that are supposedly simple for most people can be extremely complex: like hydrating an athlete in search of an Olympic medal, or feeding a newborn, in which minutes can make the difference between life and death. When a person thinks about eating it is because they are hungry. You don't think about the physiological impact on your body, just as we don't think about words, our intelligence doesn't allow us to think about the synapses necessary to generate words. Whenever possible we observe at the highest level of abstraction, guided by the law of least effort. But when we feel some difficulty, we go down to the lowest levels to try to understand the problem and look for a solution, for example, because we lack energy, or because we are feeling some discomfort, or pain in some part of the body. THE PATIENT is propagated in all the doctors who treated him throughout his life. But now that same PATIENT can centralize all his information that does not belong to him but belongs to the doctors who analyzed him. Observing this aspect is essential to reduce the complexity of the system and at the same time maintain data integrity. A person's knowledge base can be the same size as that of a medical specialist. From the patient: it is big because it has all the events in your life related to your health. From the doctor: because there are all events involving patients who have had a certain disease at some point. On the other hand, one more patient in the knowledge base can change the results of the algorithms, like the last drop that makes the water overflow the glass. The need arises to synthesize, creating a specific knowledge base to carry out the momentary diagnosis that collects all the necessary information from the patient and the experts' maps, suggests a diagnostic hypothesis, and is eliminated, without first returning the new and relevant information to the patients, recording this consultation, and in the same way, enabling the specialist to generate new analyzes from these new clinical cases, which in the future may be part of the knowledge bases of other determined diagnoses. Now imagine, millions of patients, with thousands of doctors, within a scenario of 10 thousand cataloged diseases that list more than 5 thousand symptoms, it doesn't seem WEIRD.