A Transformer Architecture for Risk Analysis of Group Effects of Food Nutrients

Abstract

In medicine, context is crucial for accurate patient diagnosis, as the same indicator can have different implications based on its setting. Transformer architecture models have not yet been applied to analyze nutritional data in patient histories. These models offer significant advantages for biomedical analysis, such as considering global context, interpreting attention weights, and generating informative input vectors. The attention mechanism’s ability to uncover multifactorial relationships can help physicians save time and concentrate on specific patterns identified by the neural network. This study adapted the encoder transformer for tabular data, applying it to classify metabolic disorders in patient history. Research into applying transformer architecture to tabular and dietary data shows great promise, yielding results that align with established medical findings while introducing innovative methods for utilizing attention and vectorizing this data.