global ecosystem analyst based on artificial intelligence. Private ecosystems to diagnose the human body in real time
This is illustrated by the World Health Organization report. Experts claim that there are many factors that affect the health of the world's population: among them are environmental damage, increased stress levels, worsening nutritional situation due to too much sugar, hormones, and chemical components used in products. Prophylaxis and prevention of disease in the future is a much cheaper and effective way to maintain health and prolong life, rather than cure disease. However, it is not always a predictable disease even if someone continues to undergo a medical examination, and most people know about ex post facto disease attacks. Lack of accurate predictions makes maintenance more complicated and more expensive, and often even makes full recovery impossible. Results of,
The situation in this area can be substantially enhanced by a combination of drugs and artificial intelligence in a syncretic mixture. Modern self-learning systems make it possible to carry out real-time comprehensive diagnoses of organisms with high accuracy, to identify previously unknown patterns between indicators of human organism activity and the risk of morbidity, to warn about the possibility of a state of acute chronic disease. Applying artificial intelligence will enable people to manage their health strategically. Because with reliable knowledge about the risks a person will be able to take the steps needed for prophylaxis of the disease. That is precisely the mission that must be fulfilled by Hai Health.
What is it Hai: Health?
Hi: Health is a global ecosystem analyst 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 health control device indicators, we teach artificial intelligence to ensure early diagnosis of various diseases and to determine unknown causal relationships between the functions of organs and body systems and disease outbreaks. AI will be able to analyze some deviations, 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 the help of Al it will be possible to monitor the effectiveness of treatments in real time and improve doctor's prescriptions.
The problem is in the medical field.
Only in the United States and the European Union, hundreds of thousands of patients die every year because of a doctor's diagnosis. The economic costs associated with complications encountered in the wrong medicines prescribed are more than $ 100 billion per year.
The main reasons for misdiagnosis are as follows:
Doctors specialize in certain organisms or organisms and often cannot see the overall picture;
The lack of experience and problems of doctors in knowledge often leads to situations, when rare diseases cannot be identified;
Lack of time so doctors must analyze medical history, the reason is that the high workload of doctors (appointment with patients) and documentation requires a lot of time;
Complexity in the definition of X-ray disease, CT, MRI studies, histological examination during non-standard types of disease, and also high dependence on subjective experience by an expert.
Based on the artificial intelligence of neural networks it will be possible to make a large number of differences in the field of medical diagnosis.
How does it work?
Opportunity for platform options for someone:
Download personal
medical data Secure and anonymous medical data storage
Respect in the form of obtaining 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 disease at an early stage
Buy and connect tested devices (gadgets) for organism diagnosis expressly
Make an appointment to undergo a medical examination
Find and buy proven drugs
Artificial intelligence capabilities 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, determine the slightest correlation between gadget changes and results human test.
Identification of patterns and sources of disease
Artificial intelligence makes recommendations for lifestyle management based on the likelihood of disease occurrence
Create individual care plans and nutrition
Control drug consumption
Track maintenance processes
Trackers for real-time data collection Rocketbody
Body temperature
Breath rhythm
Physical activity
levels Blood alcohol levels
Hemoglobin level in blood ECG
blood pressure Heart rhythm Ecosystem for doctors Online consultation for patients Sharing experiences with colleagues Collaborative patient care Monitor the truth of taking medication by patients
Online control of the patient care process
Identification of more accurate sources of disease with the help of AI
Access to neural networks for a fee.
Ecosystems for Business
Insurance companies receive more accurate accounts of possible insured events. Increase their profits by minimizing the risk of paying insurance premiums. Selling health insurance through an application
Pharmaceutical companies receive statistical reports about the sale of drugs, local diseases (urban) and the effect 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 mining data (detect titles in databases) to get patterns. In today's global competition, knowledge of patterns found can provide additional benefits.
What is a Mining Date?
Data Mining is the collective name for the combination of detection technology among previously unknown, non-trivial data, which can be interpreted as operationally useful and available from the knowledge needed to make decisions in various fields of human activity
Data mining tasks
Classification Data mining
tasks are the easiest and most common. In the results of completing the classification task, one can find indicators that characterize groups of objects from the dataset being studied (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.
grouping
Clustering is a logical follow-up for classification ideas. This task is more complicated; the distinctive feature of grouping is that the object class is not predetermined. The results of grouping divide objects into groups. Examples of methods for dealing with grouping tasks: "unsupervised learning", special types of neural networks - Kohonena's self-government map.
Road map
July - September 2017 Study problems in medicine and find solutions to develop strategic maps
October-December 2017 Write Whitepaper, develop smart contracts, create architecture and develop prototype platforms, prepare marketing strategies.
January-April 2018 Run Pre-ICO, RocketBody gadget pre-order, make a legal basis
May-August 2018 ICO Launch, Publishing & HiHealth v1.0 with functions to collect (buy) user data, partner programs with clinics and CIS laboratories,
August-January 2019 Buy medical data, process medical data, teach artificial intelligence, Buy medical data , process medical data, teach neural networks Predict the likelihood of a heart attack by analyzing various points of view (height, age, reading ECG / Echo, analysis, chronic morbidity) General diagnostic complaints or diseases based on blood chemistry symptoms and patients.
February-July 2019 Launch and publish HiHealth v2.0 with artly intelligent helpers, broker launch dates.
August 2019 Health and life insurance,
Team
Aleksandr Potkin: CEO, CFO
Salman Qadir: International Business Manager
Egor Stepanichtchev: CIO
Konstantin Rerzhukou: DEVELOPMENT OF THE SOFTWARE
Eugene Makeychik: DISIGN
Michael Zhalevich: DEVELOPMENT OF BLOKCHAIN
Eugene Koval: DEVELOPMENT OF SOFTWARE
Pavel Yeschenko: DEVELOPMENT OF BLOKCHAIN
Vladislav Vasilchyk: SYSTEM ANALYSIS
Aliaksey Mkrtychan: Volha
Hedranovich DATA SCIAINCE DEVELOPMENT: MSC DATA SCIENTIST
Andrei Lapanik: DATA SCIENCE SYSTEM ARCHITECT
More details Visit Here:
Website: https://hihealth.io/
Whitepaper: https://hihealth.io/assets/_HiHealthWPv0.1ENG.pdf
JN 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
ProfiL link:https://bitcointalk.org/index.php?action=profile;u=2447520
ETH addres: 0x845296575320897ED34C1aB7a8d92531e43e20F3
No comments:
Post a Comment