The development of computational models to support graph.md solutions in the healthcare area are based on the perspective of comprehensive knowledge mapping through knowledge modeling of healthcare institutions for telemedicine and diagnostic support. graph md has developed a disruptive solution with the best practices and existing technologies. This, considering the opinion of important world-class experts in the area of Graph Visualization, Knowledge Management, and Complex Systems technology. Likewise, we want to contribute to making a difference motivated by the example of these renowned researchers. This aspect is very important for the Health Knowledge Synthesis project. We would like to have the opportunity to integrate your healthcare institution into the graph.md digital ecosystem. PhD in Neuroscience to develop a computational model to support the diagnosis of Epilepsy and 10-year follow-up of operated patients. Analysis of healthcare data to develop the knowledge base and implement the Electronic Patient Record. Multiple visualization perspectives were developed, providing a real analysis of the concepts (nodes) and their relationships. Research and development in the area of Computer Science applied to medical diagnostic systems through the execution of innovation projects in partnership with clinical hospitals and other research centers around the world.
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We use knowledge management concepts to transform each interaction into effective knowledge, a fundamental basis for building a new culture related to AI applied to the digital universe. graph.md complete Graph Data Science solution in the Healthcare area was integrated with neo4j enterprise, allowing the execution of graph algorithms to predict edges, nodes, and relationships generated by the algorithms within our Synthetic universe. The patient and the doctor became the center of the entire process. We created the basic idea of providing contextualized knowledge at the appropriate moments for clinical decision-making. The algorithms are processed in the areas of medical specialties based on their records, and on the records of patients approximately equal to their condition, already with an assigned diagnosis. In this way, it is possible to correlate the analyzed patient with the most relevant diagnostic possibilities.
In the Graph Data Science universe, each doctor and/or patient can have several events with records (logs) of the entire extent of the interaction in their knowledge synthesis base. Thus, it is possible to follow a path of 50 symptoms and relate them to a person based on 50 thousand concepts and 500 thousand relationships. In our graph.md super app, patient data arrives at the knowledge synthesis console, the person interacts in a sanitized zero-knowledge environment and chooses the symptoms for their current state. With dynamic interaction, it is possible to create schema models supported by intelligent agents, seeking a more meaningful experience for the patient who becomes part of the process of building their knowledge base simply and intuitively. The goal is to develop plugins for the graph.md super app with direct access for doctors and patients. Each plugin has a specific function to solve a diagnostic problem.
The idea is to make the main diagnoses available within the Graph Data Science universe. In the graph.md super app, we identify possible diseases based on the symptoms reported. Subsequently, we suggest content related to clinical information and the main supplements and professionals who can help in these specific cases. We have observed that in other apps, content production is based on a decontextualized timeline. Our structure for identifying related concepts in a graph database can bring people closer to the desired solution, in this case, a medical diagnosis. All predictions are based on relationships through graph algorithms. In this sense, an intelligent basis is created to identify people who are approximately equal in the aspects measured by the GDS.
Graph Knowledge structures allow a new approach to visualization in mental maps, with more intuitive interaction possibilities in the super app graph.md to assist in the medical diagnosis of patients, always seeking to add value to the process of identifying and treating diseases. The proposed technology supports BiTemporality, which is the joint identification of the current record and the previous state of the information. This fact allows us to support the creation of personal knowledge bases and the synthesis of this knowledge in rooms through specific applications, created by graph md in the form of plugins, available in the super app graph.md to propose preventive initiatives to patients, identifying the main risk factors related to specific analyses of the individual aspects of each person's behavior.
contact@graph.md to get more information on graph md points of view.
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