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
Musicality of breath
Physical action level
Blood liquor level
The level of hemoglobin in the blood
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 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.
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 SYSTEM ARCHITECT
Visit the links below for more information:
ANN Thread: https://bitcointalk.org/index.ph…
Authored by Lelvin: https://bitcointalk.org/index.php?action=profile;u=1275173