Learning about a user

1. At Nostra Biome, we use a combination of regular check-ups and user-provided data to generate a comprehensive understanding of each user's health.

2. By performing monthly checks on the user's blood and stool samples, we gather critical information about their overall health, including any signs of disease or infection.

3. In addition to these physical measures, we also ask users to fill out a questionnaire to gather data about their lifestyle, diet, and other factors that may influence their health.

4. All of this data is then fed into our algorithm, which generates a small model of each user's health progression over time.

5. This model is continuously updated with new data from each check-up, as well as any changes or updates provided by the user.

6. The algorithm also incorporates a signaling-damage-measurement mechanism that assigns a criticality score to each data point we track.

7. This criticality score takes into account the level of impact that each data point has on the overall health of the user.

8. The scoring mechanism is based on a 7-layer model that weighs the importance of each data point in relation to the user's overall health.

9. By continuously training our algorithm against this scoring mechanism and the user models we generate, we can accurately track each user's health progression over time.

10. The user models are also stored in our system, allowing us to leverage historical data about treatment to further refine and improve our algorithm's ability to predict and prevent health issues.