Data Design in Biomedical AI/ML

Data Design refers to the systematic choice of what data are modeled for analysis and how these data and the model output(s) are mapped between the Problem Space (real-world) and the Model Space (features for the ML modeling). ML data design is an essential element of ML modeling. ML data design differs from classical statistical, epidemiological etc. study designs in that (a) ML data design relies heavily on the existence of digital data repositories that are created independently of the problem solving intent at hand, (b) ML modeling is highly scalable and mostly automated, (c) when using experimental data, ML data design may be used to guide the experiments conducted, (d) uses a richer set of data representations that transcend the classical design matrices such as text, relational databases, graphs etc.; and (e) ML modeling has its own distinct capabilities, limitations and other properties and these are reflected in the data design choices. The present chapter covers tried and tested strategies and protocols that contribute to successful data designs and addresses a number of important biases that threaten validity and generalizability of results. Lower level data transformations, data storage, and security aspects are covered in the “Data Preparation, Transforms, Quality, and Management” chapter.