MetaMathModelling: A generative AI and human-in-the-loop data-centric modelling for science

Human health phenomena are complex and vary naturally with time. Sensors are broadly used to monitor human health for predictive health monitoring. These rapidly expanding quantities of sensor data can reside within electronic health records (EHR), collected throughout people's lives. The dynamics of healthcare sensor data are both complex and difficult to model. Scalable and explainable models must be developed for these high-dimensional, noisy, artefactual time-series data – typically collected under different user environments – and help humans understand the biological and physical rationale behind the diseases and their progressions.

Mathematical models empowered by Large Language Models and agent-based simulations can help, in theory. Agents in a simulation system are experts with domain knowledge and skills that can take action. It is a well-established method that is often used in engineering and project management. Agents and their actions are often managed by an organisational network that can be present as graphs (e.g., DAG), decision trees, or logic that can then be manufactured as a circuit board. Nature is complex, and our data are limited. Therefore, mathematical and statistical modelling approaches (e.g., linear regression and Gaussian mixture models) were often outperformed by deep learning approaches. However, when we found the model performance between explainable mathematical modelling approaches (white-box) and non-explainable approaches (black-box) are similar, we know that we have understood the principle components of factors (or confounders) in the feature space, which will then allow us to explore the new knowledge in the feature space and confirm the novelty detection decision boundaries that are highlighting extreme cases, e.g., a case of rare disease.

I have generated an agent-based tool called MetaMathModelling: https://chat.openai.com/g/g-DOCgzESMk-metamathmodelling This tool used the intuition of GPT4 (as a consortium of experts in domains range from data scientist to policy makers) and make decisions with human in the loop. Meta learning is used to optimise the model performance after taking the decisions from LLMs and human. Please try it and let me know your experience. Please save your results safely before close each session, as your data will not be stored by the OpenAI nor the MetaMathModelling.

Tell me you experience using MetaMathModelling

Change Image

• It took me 60 mins to build a model
• It took me 30 mins to build a model
• It doesn't work
• I have more feedback to share and will send a message