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 train artificial intelligence conduct early diagnosis of various diseases and to detect the previously uncovered cause-and-effect communication between the functioning of systems and organs of the body and the occurrence of diseases. AI will be able to analyze minimal, inconspicuous to the human eye, deviations indicators from the norm, and also to obtain 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.

What is Hi:Health?

Hi:Health is a global ecosystem analyst based on artificial intelligence. The personal ecosystem for diagnosing a human body in real time.

Applying medical reports of great amount 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 functioning 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 would be 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 annually 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 main 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;

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

Lack of time that doctor has for analyzing 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 according to X-ray, CT, MRI studies, histological examination during nonstandard kind of disease, and also high dependence on subjective experience by an expert.

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

How it works?

Opportunities Options of the platform for a person:

Downloading personal medical data

Secure and anonymous storage of medical data

Rewarding in the form of getting tokens (tokens allow extending the application functionality, purchasing health and life insurance)

Anonymous sales of your data for platform tokens

Analysing 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 ability of artificial intelligence when using algorithms to analyze IR radiation

AI algorithms analyse the data obtained, based on the experience of thousands of doctors around the world 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 nutrition plan

Controls the consumption of medications

Tracking the treatment process

Tracker for real-time data collection Rocketbody

Body temperature

Rhythm of breath

Physical activity level

Blood alcohol level

The level of hemoglobin in the blood

Blood pressure

ECG

Heart rhythm

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 calculation of the probability of occurrence of an insured event. Increase their profits by minimizing the risks of paying insurance premiums. Selling health insurance through applications

Pharmaceutical companies receive statistical reports on the sales of medicines, typical 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 according to 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 obtain patterns. In the current global competition, the knowledge of the discovered patterns can give additional advantage

Data Mining is a collective name for combination of technologies that detect among the data previously unknown, non-trivial, operationally useful and available interpretations of the knowledge required for making decisions in various spheres of human activity

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 on real data.

Data mining tasks

The classification

The easiest and most common 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). According to 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, induction of decision tree, neural networks.

Clustering

Clustering is the logical 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 initially. The result of clustering is dividing objects into groups. An example of method for dealing with the task of clustering: “unsupervised learning”, a special kind of neural networks – self-organizing map Kohonena.

Roadmap

July – September 2017 Studying problems in medicine and finding solutions to develop a strategic map

October-December 2017 Writing Whitepaper, developing a smart contract, creating an architecture and developing a prototype platform, preparing marketing strategy.

January-April 2018 Run Pre-ICO, pre-order gadget RocketBody, create legal base

May-August 2018 Launching the ICO, publishing &HiHealth v1.0 with the functionality to collect (purchase) user data, partner programs with clinics and CIS laboratories,

August-January 2019 Buying medical data, processing medical data, teaching artificial intelligence, Buying medical data, processing medical data, teaching neural networks Prediction of possible heart attack by analyzing variety of viewpoints (height, age, EKG/Echo readings, analyses, chronic morbidity) Diagnostics of common complaints or diseases based on blood chemistry and patient symptoms.

February-July 2019 Release and publish HiHealth v2.0 with a personal artificially intelligent helper, launch broker’s date.

August 2019 Health and life insurance,

Team

Aleksandr Potkin: CEO, CFO

Salman Qadir: International Business Manager

Egor Stepanichtchev: CIO

Konstantin Rerzhukou: SOFTWARE DEVELOPMENT

Eugene Makeychik: DISIGN

Michael Zhalevich: BLOCKCHAIN DEVELOPMENT

Eugene Koval: SOFTWARE DEVELOPMENT

Pavel Yeschenko: BLOCKCHAIN DEVELOPMENT

Vladislav Vasilchyk: SYSTEM ANALYST

Aliaksey Mkrtychan: DATA SCIAINCE DEVELOPMENT

Volha Hedranovich: MSC DATA SCIENTIST

Andrei Lapanik: DATA SCIENCE SISTEM ARCHITECT

More details Visit Here:

Website: https://hihealth.io/

Whitepaper: https://hihealth.io/assets/_HiHealthWPv0.1ENG.pdf

ANN Thread: https://bitcointalk.org/index.php?topic=3252889.msg33879027#msg33879027

Telegram: https://t.me/HiHealth0

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

Twitter: https://twitter.com/hihealthapp

Bitcointalk Profil: https://bitcointalk.org/index.php?action=profile;u=1229230

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