Challenging the rational reduction of analysis costs

Our challenge is to obtain high-precision inference even from minimal input data.

Replica Generator(RG-Ⅲ)enables analysis AI to receive a large amount of prediction data by generating highly accurate approximated data (replicas) based on a small amount of health data from a small number of individuals (vital data, blood data, body composition, etc.) through mathematical and statistical methods. This contributes to high-precision analysis/prediction estimates that do not require endless sample collection.

Replica Generator (RG-III™) increases the number of sample data required for AI analysis by approximately 10 to 100 times while maintaining the statistical characteristics of the original actual data set through mathematical and statistical methods. The error rate is minimized when there is correlation between the data. This enables a significant reduction in the enormous time and cost required for collecting conventional data samples for analysis. The system calculates the XY2-axis correlation for all given samples and generates highly accurate replicas of the target data by imposing constraints such as population characteristics, variance, and probability distribution. For example, by grouping the pre-disease condition of lifestyle-related diseases and using population characteristics, variance, and probability distribution as constraints, it is possible to generate effective and meaningful replicas that can be treated similarly to actual collected samples. (This does not mean that the system can generate special replicas for one in a million people.)

Visualization of health through heatmaps.

By comparatively analyzing well-known and concerning disease items, it is possible to visualize disease risks.

We believe that risk factor analysis is necessary for improving and promoting health, although we do not diagnose diseases as a company. When there is a risk of hypertension, predicting the risk based on one item such as blood pressure may not be accurate enough. Health impediments could result from issues such as nutritional balance, stress, and problems with nitric oxide (NO) production, which is necessary for maintaining blood vessel elasticity beyond one's age. It could also be due to autonomic nerve disorders caused by stress and anxiety, or the partial deposition and generation of lipids on endothelial cells, which could increase blood flow resistance. By visualizing the correlation between each health data point through deviation value standardization and gradient-based visualization, we adopt a method that relates potential disease risk candidates and patterns to improve prediction accuracy.

By visualizing and understanding whether one is moving towards disease or health in a comprehensive manner, it is possible to change behavior, achieve a scientifically sound improvement, maintenance, and promotion of health, and reduce disease risks efficiently.

Trace and avoid the process of increasing disease risk from pre-disease conditions.

Disease risk heatmaps patternize each item based on the deviation value and correlation coefficient, as well as other factors, using color gradients for gender, age, and individual differences (BMI, autonomic nerve balance, blood condition, vital signs, etc.). Additionally, vast amounts of information indicating trends in well-known diseases exist in past papers and research results.

The characteristics of disease risk vary greatly depending on the disease. Until a healthy person is finally diagnosed with a disease, there are many twists and turns, and various disease risks are adjacent to one another. We believe that each disease has its own specific disease risk, but when it comes to health, we need to consider how well the cells, blood vessels, and immune systems that function within the body to maintain health are functioning complementarily. Although there has been a tendency to think that aging naturally leads to a decline in cellular metabolism and an increase in disease risk, the latest health science has shown that it is possible to slow down and prevent aging through lifestyle changes, and that age itself is not the dominant factor. The same is true for genetic issues, and there is an increasing number of researchers who believe that it is possible to avoid an increase in disease risk and achieve sufficient health improvement, maintenance, and promotion. We strongly support this way of thinking and aim to assist each individual in achieving their own health improvement, maintenance, and promotion.

Measure the changes in each parameter until a healthy person, A, becomes ill.

Confirm the current health position and predict disease risks through heatmaps, transition maps, and vector maps.

Predict the changes in each parameter for person A and estimate disease risks.

If the current, near-future, and future health status and disease risks are known,

n the upcoming era of super health, research on precision healthcare, data analysis, and understanding of health science will advance dramatically, enabling more precise prediction of future disease risks. It will become common sense for everyone to reduce or avoid disease risks in ways that fit their own patterns and to lead healthy and bright social lives. Even if the disease risk is high, prevention through behavior change will be possible without resorting to severe treatment, making it much easier to maintain Quality of Life (QoL) than it is today. At AXION Research, we are steadily working on what we can do for that future and collaborating with partners and everyone to achieve realistic development for a sustainable society.