us.graph.md - clinic

Through our experience, we can model your Diagnostic knowledge based on the graph.md platform to be applied directly to your patients. The main concepts used in the design of the graph.md Knowledge Synthesis project is based on the new generation of super apps expected for the coming years, which now aim exclusively at building personal knowledge bases. The basic idea is to concentrate technological efforts to provide a personalized and customized digital environment for each doctor and/or patient within graph.md, allowing centralization from the individual's point of view and storage of all information securely on a high-performance platform.

The computational model proposed by graph md for the healthcare project is entirely based on the universe of Knowledge Management and focuses on building personalized bases in Graph Data Science using multidisciplinary concepts from the areas of computing and neuroscience. [clinic e1 audiobook]

Once the appropriate modeling has been performed on the contents of the instances of each mapped element, the symptom and disease groups emerge. These are distinctive 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 high-impact scientific studies. Thus, the technological innovation of the model presented by graph md is structured on a unique platform with support for AI and temporality. The 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 the modeling based on instances, it is clear that a system of this size in the health area must be understood as complex. Therefore, the knowledge engineer can't predict all of its possibilities, just from its modeling. Simulation and modeling techniques for complex systems are necessary for their definition and development. Due to the complexity of this topic, it may be useful to see the series on our website: mind, thinking, and complex brain. Likewise, we are available to present the basis of graph.md for any questions or provide any necessary clarifications.

The graph md schema diagram of the proposed model for knowledge synthesis in the healthcare field shows that each edge is assigned to a significant set of disease and symptom instances; the "Groups" elements are part of the previous organization. The "Grouped" elements were generated from the processing of the GDS (Graph Data Science) algorithms. It is worth noting that the base elements form a triangle between "Patients", "Symptoms" and "Diseases". The Groups and clusters, therefore, are satellite elements of this triad. In this sense, synchronization is established between the organization of the reasoning of specialist doctors and the Artificial Intelligence algorithms. [clinic e2 audiobook]

Graph MD's work is deeply connected to the area of AI applied to healthcare. We have developed an innovative telemedicine architecture in Graph Data Science, using a Neo4j graph database. The graph.md example shows that in other areas, modeling a complex system applied to AI must observe the flows of occurrences of relationships between people's events and maps of companies' needs. By developing highly complex systems throughout our trajectory, we believe we can contribute with Graph Data Science technology, creating solutions in this scenario of the beginning of the Artificial Intelligence era directly linked to people's daily lives. This Graph DB designed by Graph MD can also be used by your institution. The knowledge base developed includes all diseases mapped in the ICD (International Classification of Diseases), containing 4 thousand symptoms. As well as all the due relationships between patients, symptoms, and diseases, information from more than a thousand scientific articles from the Pubmed platform and MeSH (Medical Subject Headings) was categorized to build the database. The model is available in the Graph MD repository on GitHub. However, if you prefer, please contact@graph.md and we will be happy to present the diagnostic support model created by graph md to your institution and your clinical staff. Through the graph.md 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 neo4j graph database, it can seem like a complex process. Therefore, we are available to facilitate the process of viewing the developed model in operation within the graphh.md super app. [clinic e3 audiobook]

Data from the graph.md model:

4 thousand Symptoms;
22 thousand Disease Terms;
13 thousand Anonymized Patients;
16 thousand Patterns (/Clusters) recognized by GDS algorithms;
433 Related Disease Groupings Recognized by GDS Algorithms;
1.5 thousand Symptom Attribution Events attributed to patient X;
4.7 thousand Symptom Terms 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 Symptom-Disease relationships;
103 thousand Symptom associations related to Diseases;
4.2 Source Symptoms to Target Grouped Symptoms relationships;
53 thousand Grouped Disease Relationships for Diseases; 25 thousand Diseases that make up their relationships;
7.9 thousand Grouped Symptom relationships for Grouped Diseases;
25 thousand Grouped Disease relationships for Other Grouped Diseases;
76 thousand Diseases for Grouped Diseases;
26 thousand Symptom relationships for Grouped Disease relationships;
2 thousand Grouped Symptom relationships for Disease relationships;
3.3 thousand Grouped Symptom relationships for Grouped Disease relationships;
1 thousand Disease terms associated with ICD subgroups.

get in touch

contact@graph.md to get more information on graph md points of view.

Av. João Pessoa, 731 - Sala 802 - Cep: 90.040-000 - Porto Alegre, Brazil