TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling

Understand

Tabular medical records remain the most readily available data format for applying machine learning in healthcare.

• However, traditional data preprocessing ignores valuable contextual information in tables and requires substantial manual cleaning and harmonisation, creating a bottleneck for model development.
• We introduce TabText, a preprocessing and feature extraction method that leverages contextual information and streamlines the curation of tabular medical data.
• This method converts tables into contextual language and applies pretrained large language models (LLMs) to generate task-independent numerical representations.