us.graph.md - patient

Dear Patient P! After analyzing your data in graph.md, we would like to schedule an in-person consultation with Specialist Doctor E to avoid (or reduce) the risk of a certain disease D occurring.

The main challenge is that health is not explicitly defined in the diagnostic process. It is considered only as a complement to disease. People mistakenly and invariably think: if I am not sick, then I am healthy! But the light at the end of the tunnel is that by studying symptoms throughout life, it can be observed that the patient's physiology relates to the two universes: Health and Disease.

By observing a patient under the magnifying glass of a knowledge base in graph.md, we can predict that if the physiology is functioning perfectly, the disease is a certain safe degree away from the patient. If we eventually identify early warning signs in certain physiological dysfunctions, we can intervene to propose a return to this desired state even before the disease affects the patient. [patient e1 audiobook]

We are at an important moment in the appreciation of information ownership. The collective unconscious feels that people want to be able to own their data, but the way it is used as proposed by the current model does not allow this. Systems are developed by companies only to perform the most diverse processes and operational procedures for recording. People's data are nothing more than instances of these relational banks, just tiny cells in a continuous flow of infinite development of new versions to serve more and more consumers. Within this paradigm, people's ownership of information will be nothing more than a distant dream. It will never cease to be a utopia. And why not, if we think about its actual non-realization, a dystopia?

Impressively, this unsolvable problem in the current context of achieving a better level of computational intelligence is simple to solve if we focus our efforts on developing people's systems. To do so, a communication protocol between the parties is necessary. With management and control systems for moving data between individual graph md and specific groups of people, or even collective rooms of thematic groups segmented by areas of knowledge. In any case, the decisive factor in this case is the centralization and control of data for people who will have real ownership of their knowledge bases.

With advanced graph path filters, we work with the concept of complete interaction of events. Each person has multiple events with records of the entire extent of the interaction in their graph md. Translating this concept, we can say that it is possible to follow a path of 50 concepts and relate them to a person from 50 thousand concepts. With database balancing, it is also possible to define the concepts of precision and recall to define the self-constructiveness of the bases and equate them with high performance in pattern recognition. [patient e2 audiobook]

Where is your Data? Shouldn't it be in our personal databases?

But do we have a personal knowledge base?

Unfortunately, not today!

But now, why don't we have one?

A smart proposal for effective storage and centralization in the cloud of all your medical data on the graph.md platform.

A characteristic that is strange today is that people do not know where their data is. It flies around the world and is only accessed through login. Everything converges before the user interacts with the various applications and websites whose sole purpose is to purchase. This process ends, the logout happens and the data disappears, no one knows where their data is. Except for the sad set of steps that describe the application screens, but what if the doctor asks if you have ever had a certain symptom? Then the truth comes out! People do not own their data. They do not even know it exists! The company's systems are made with specific objectives of mass production and consumption. They are not modeled and treated correctly to be synthesized in knowledge bases. A simple change of focus, from the company to the person, makes all the difference here! Seeking to build knowledge bases for people and groups of users with clear and concrete objectives. Allowing users to manage this synthetic knowledge base makes all the difference. And to think that graphs emerged in the early days of computing. Today in Graph Data Science we use some algorithms defined in the 70s, or even from earlier times. With the advancement of knowledge engineering combined with the visualization of complex data proposed by visualization APIs, we seek to contribute to the development of the super app graph.md. And if we think formally about a complex system, the objective would be compatibility, within the unification proposition proposed by Von Neumann who, before his untimely death, had the idea of integrating the current architecture that bears his name with that of the automaton architecture, also defined by him. Now, leaving formalism aside for a moment, in the explanatory sense, the processing would be in the graph itself. It is worth remembering that even back then the genius saw that the models were compatible. [patient e3 audiobook]

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