HiHealth Global Medical Ecosystem


HiHealth is a global medical ecosystem based on artificial intelligence for complex personalized diagnosis of the organism in real time. The personal ecosystem for diagnosing a human body in real time. Finds sources, patterns of development of different diseases and prevents future illnesses. Using the data of medical surveys of a large number patients, as well as indicators gadgets for control state of health, we prepare artificial intelligence direct early diagnosis of various diseases and to detect the previously uncovered cause-and-effect correspondence between the working of systems and organs of the body and the occurrence of diseases. AI will be able to analyze negligible, inconspicuous to the human eye, deviations indicators from the standard, and also to get more accurate results of examinations (eg. ECG) as a result of their cleaning from the noise generated by the instruments. Also with the help of AI can be monitored in real time the effectiveness of treatment and adjust the appointment of a doctor.

Applying medical reports of great measure of patients, and also indicators of health-control gadgets, we teach artificial intelligence to ensure early diagnosis of different illnesses and determine previously unidentified cause-and-effect relationship between working of body organs and systems of the body and outbreak of diseases. AI will be able to analyze slightest deviations, which human can’t notice, and also to get more accurate survey results (for example, electrocardiogram) resulting from clearing devices of noise. Also with the help of Al it is possible to monitor effectiveness of treating in real time and correct doctor’s prescriptions.

The problem in the field of medicine

Just in USA and EU hundreds of thousands of patients die yearly due to doctors’ misdiagnoses. The economic cost connected with complications that encountered in wrong prescription of drugs is more than $100 billion per year.

The primary reasons of misdiagnoses are as follows:

The doctors are specialized in certain organs or organism’s system and often can’t see the overall picture;

Absence of experience and doctors’ problems in knowledge often lead to situation, when rare diseases can be not identified;

Absence of time that doctor has for breaking down medical history, the reason is doctor’s high workload (appointments with patients) and also documentation takes significant amounts of time;

The complexity in the definition of the disease as per X-beam, CT, MRI studies, histological examination amid nonstandard sort of disease, and also high dependence on subjective experience by an expert.

Based on neural networks artificial intelligence will allow to make a huge measure of difference in the field of medical diagnosis.


How it works?

Downloading personal medical data

Secure and anonymous storage of medical data

Rewarding through getting (tokens allow extending the application usefulness, purchasing health and life insurance)

Anonymous sales of your data for platform tokens

Dissecting data using artificial intelligence for diagnosing diseases at early stages

Purchasing and connecting tested devices (gadgets) for express diagnosing of the organism

Making appointments for undergoing medical examination

Searching and purchasing proven drugs

The capacity of artificial intelligence when using algorithms to analyze IR radiation

AI algorithms analyze the data obtained, based on the experience of thousands of doctors far and wide and millions of studies, determining the slightest correlation between the changes in gadgets and the results of human tests.

Identifies the patterns and sources of a disease

Artificial Intelligence makes recommendations for lifestyle management based on the possibility of disease occurrence

Creates an individual treatment and sustenance design

Controls the consumption of medications

Following the treatment process

Tracker for real-time data collection Rocketbody

Body temperature

Musicality of breath

Physical action level

Blood liquor level

The level of hemoglobin in the blood



Heart musicality

Ecosystem for a doctor

Online consultations of the patients

Sharing of experience with colleagues

Collaborative patients’ treatment

Monitoring the correctness of taking medication by patients

Online controlling the process of patients’ treatment

Identifying the more accurate source of the disease with the help of AI

Access to neural networks on a fee basis.

The Ecosystem for Business

Insurance companies receive a more accurate count of the likelihood of occurrence of an insured event. Increase their profits by limiting the risks of paying insurance premiums. Selling health insurance through applications

Pharmaceutical companies receive statistical reports on the sales of medicines, regular regional (urban) diseases and the effects of medicines on a person. In order to personalize the treatment, the data can be obtained from the DNA database about the predisposition of a person to certain diseases as indicated by his/her geographical residence

Clinics improve the methods of treatment and prevention of human diseases

Research centers and developers can use the benefits of data mining (the detection of titles in databases) in order to get patterns. In the current global competition, the knowledge of the discovered patterns can give extra advantage

What Date Mining is?

Data Mining is a collective name for mix of technologies that detect among the data previously obscure, non-unimportant, operationally useful and available interpretations of the knowledge required for settling on decisions in various spheres of human action

Data Mining technologies are a powerful apparatus of modern business analytics and a data research for finding hidden patterns and building forecast models. Data Mining is based not on speculation, but rather on real data.

Data mining tasks

The classification

The easiest and most regular Data mining task. In the result of completing the task of classification, one can discover indicators that characterize groups of objects of investigated dataset (classes). As per these indicators the new object can be classified.

The methods for dealing with the task

In order to complete the task of classification one can use some methods including Nearest Neighbor, k-Nearest Neighbor, Bayesian Networks, enlistment of decision tree, neural networks.


Clustering is the legitimate follow-up to the idea of classification. This task is more complicated; the characteristic feature of clustering is that classes of objects are not predetermined at first. The result of clustering is partitioning objects into groups. An example of method for dealing with the task of clustering: “unsupervised learning”, a special sort of neural networks – self-arranging map Kohonena.

HiHealth Team

Aleksandr Potkin: CEO, CFO

Salman Qadir: International Business Manager

Egor Stepanichtchev: CIO

Konstantin Rerzhukou: SOFTWARE DEVELOPMENT

Eugene Makeychik: DISIGN




Vladislav Vasilchyk: SYSTEM ANALYST


Volha Hedranovich: MSC DATA SCIENTIST



Visit the links below for more information:

Website: https://hihealth.io/

Whitepaper: https://hihealth.io/assets/_HiHe

ANN Thread: https://bitcointalk.org/index.ph

Telegram: https://t.me/HiHealth0

Facebook: https://www.facebook.com/hihealt

Twitter: https://twitter.com/hihealthapp


Authored by Lelvin: https://bitcointalk.org/index.php?action=profile;u=1275173


#blockchain #crytpocurrency #ethereum #ico #hihealth


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