Hi: Health | Ecosystem to diagnose the human body in real time

HIHealth is a global medical ecosystem based on artificial intelligence for complicated personal diagnosis of organisms in real time. Private ecosystem to diagnose the human body in real time. Find sources, patterns of development of various diseases, and prevent future diseases. Using medical survey data from a large number of patients, as well as gadget indicators to control health conditions, we train artificial intelligence to make early diagnoses of various diseases and to detect causal communication before found between the functions of the system and organs of the body and the occurrence of disease. AI will be able to analyze minimal, inconspicuous human eyes, deviate indicators from the norm, and also to obtain more accurate examination results (eg ECG) as a result of their cleansing of the noise produced by the instrument. Also with the help of AI can be monitored in real time the effectiveness of treatment and adjusting the appointment of a doctor.

What is Hi: Health?
Hi: Health is an analyst of global ecosystems based on artificial intelligence. Private ecosystem to diagnose the human body in real time. Applying medical reports to a large number of patients, as well as indicators of health controls, we teach artificial intelligence to ensure early diagnosis of different diseases and determine unidentified cause-and-effect relationships between bodily organs and body systems and disease outbreaks. AI will be able to analyze slight irregularities, which humans cannot pay attention to, and also to get more accurate survey results (for example, electrocardiograms) that result from cleaning the device from noise. Also with Al's help it will be possible to monitor the effectiveness of treating in real time and the correct doctor's prescription.

The problem is in the field of medicine
Only in the United States and the EU, hundreds of thousands of patients die each year because of a doctor's misdiagnosis. Economic costs associated with the complications encountered in prescription drugs are more than $ 100 billion per year. The main reasons for misdiagnosis are as follows:

Doctors specialize in certain organs or systems of organisms and often cannot see the overall picture;
Lack of experience and problems of doctors in knowledge often leads to a situation, when rare diseases cannot be identified;
The lack of time that doctors have to analyze medical history, the reason is that the doctor's workload is high (patient appointments) and documentation takes a lot of time;

Complexity in the definition of disease according to X-ray, CT, MRI studies, histological examination during non-standard types of disease, and also high dependence on subjective experience by an expert.
Based on artificial intelligence, neural networks will make it possible to make a large number of differences in the field of medical diagnosis.

How does that work?

Platform choice opportunity for someone:

Download personal medical data

Safe and anonymous medical data storage

Appreciate in the form of getting tokens (tokens allow expanding application functions, buying health and life insurance)

Anonymous sales of your data for platform tokens

Analyzing data using artificial intelligence to diagnose the disease at an early stage

Buy and connect the tested device (gadget) to diagnose the organism expressly

Make an appointment to undergo a medical examination

Search for and buy proven drugs

Artificial intelligence ability when using algorithms to analyze IR radiation

The AI algorithm analyzes the data obtained, based on the experience of thousands of doctors around the world and millions of studies, determining the slightest correlation between changes in gadgets and human test results.

Identify patterns and sources of disease

Artificial intelligence makes recommendations for lifestyle management based on the likelihood of disease

Creating individual care plans and nutrition

Control the consumption of drugs

Track the treatment process

Tracker for Rocketbody real-time data collection

Body temperature
Breath rhythm
Level of physical activity
Blood alcohol level
The level of hemoglobin in the blood
Blood pressure
ECG
Heart rhythm

Ecosystem for doctors

Online consultation for patients
Share experiences with colleagues
Collaborative patient care
Monitor the truth of taking medication by patients
Online controls the patient care process
Identify a more accurate source of disease with the help of AI
Access to neural networks for a certain fee.

Ecosystem for Business

The insurance company receives a more accurate calculation of the possibility of an insured event. Increase their profits by minimizing the risk of paying insurance premiums. Sell health insurance through the application

Pharmaceutical companies receive statistical reports about the sale of drugs, diseases typical of the region (urban) and the effects of drugs on someone. To personalize treatment, data can be obtained from the DNA database about a person's predisposition to certain diseases according to their geographical residence.

Clinics improve methods of treatment and prevention of human diseases

Research centers and developers can use the benefits of data mining (detection of titles in the database) to get patterns. In today's global competition, knowledge of patterns found can provide additional benefits

What is the Mining Date?

Data Mining is a collective name for a combination of technologies that detects between previously unknown, non-trivial, operationally useful interpretations and is available from the knowledge needed to make decisions in various fields of human activity

Data Mining Technology is a powerful tool of modern business analytics and data research to find hidden patterns and build forecast models. Data Mining is not based on speculation, but on real data.

Data mining tasks

Classification The easiest and most common data mining task. In the results of completing a classification task, one can find indicators that characterize the group of objects from the studied dataset (class). According to this indicator new objects can be classified. Methods for handling tasks To complete a classification task one can use several methods including Nearest Neighbor, k-Nearest Neighbor, Bayesian Networks, decision tree induction, neural networks. Clustering Clustering is a logical follow-up to the classification idea. This task is more complicated; a distinctive feature of grouping is that object classes are not predetermined. Grouping results divide objects into groups. Examples of methods for handling grouping tasks: "unsupervised learning",

Roadmap

July - September 2017 Study problems in medicine and find solutions to develop strategic maps. October-December 2017 Writing Whitepapers, developing smart contracts, creating architectures and developing prototype platforms, preparing marketing strategies. January-April 2018 Run Pre-ICO, RocketBody pre-order gadget, for legal basis May-August 2018 Launch of ICO, publishing & HiHealth v1.0 with the function to collect (buy) user data, partner programs with CIS clinics and laboratories, August -January 2019 Buy medical data, process medical data, teach artificial intelligence, buy medical data, process medical data, teach neural networks Predict the possibility of a heart attack by analyzing various points of view (height, age, ECG / Echo reading, analysis, chronic morbidity ) Diagnostic general complaints or illnesses based on blood chemistry and patient symptoms. February-July 2019 Releasing and publishing HiHealth v2.0 with personally artificial intelligent helper, launch broker date. August 2019 Health and life insurance,

Team

Aleksandr Potkin: CEO, CFO

Salman Qadir: International Business Manager

Egor Stepanichtchev: CIO

Konstantin Rerzhukou: DEVELOPMENT OF SOFTWARE

Eugene Makeychik: DISIGN

Michael Zhalevich: BLOKCHAIN DEVELOPMENT

Eugene Koval: DEVELOPMENT OF SOFTWARE

Pavel Yeschenko: BLOKCHAIN DEVELOPMENT

Vladislav Vasilchyk: SYSTEM ANALYSIS

Aliaksey Mkrtychan: SCIAINCE DATA DEVELOPMENT

Volha Hedranovich: MSC DATA SCIENTIST