The use of synthetic data is recognized as a crucial step in the development
of neural network-based Artificial Intelligence (AI) systems. While the methods
for generating synthetic data for AI applications in other domains have a role
in certain biomedical AI systems, primarily related to image processing, there
is a critical gap in the generation of time series data for AI tasks where it
is necessary to know how the system works. This is most pronounced in the
ability to generate synthetic multi-dimensional molecular time series data
(SMMTSD); this is the type of data that underpins research into biomarkers and
mediator signatures for forecasting various diseases and is an essential
component of the drug development pipeline. We argue the insufficiency of
statistical and data-centric machine learning (ML) means of generating this
type of synthetic data is due to a combination of factors: perpetual data
sparsity due to the Curse of Dimensionality, the inapplicability of the Central
Limit Theorem, and the limits imposed by the Causal Hierarchy Theorem.
Alternatively, we present a rationale for using complex multi-scale
mechanism-based simulation models, constructed and operated on to account for
epistemic incompleteness and the need to provide maximal expansiveness in
concordance with the Principle of Maximal Entropy. These procedures provide for
the generation of SMMTD that minimizes the known shortcomings associated with
neural network AI systems, namely overfitting and lack of generalizability. The
generation of synthetic data that accounts for the identified factors of
multi-dimensional time series data is an essential capability for the
development of mediator-biomarker based AI forecasting systems, and therapeutic
control development and optimization through systems like Drug Development
Digital Twins.

本文讨论合成数据在神经网络人工智能系统开发中的重要作用，并探讨复杂多尺度机制模拟模型生成多维时间序列数据在生物医学领域中的应用，尤其是对于疾病预测和药物开发流程的重要性。

为基于人工智能的疾病轨迹预测和药物开发数字孪生生成多维分子介质时间序列数据：考虑因素

Generating synthetic multi-dimensional molecular-mediator time series data for artificial intelligence-based disease trajectory forecasting and drug development digital twins: Considerations