  Tabular data is often hidden in text, particularly in medical diagnostic
reports. Traditional machine learning (ML) models designed to work with tabular
data, cannot effectively process information in such form. On the other hand,
large language models (LLMs) which excel at textual tasks, are probably not the
best tool for modeling tabular data. Therefore, we propose a novel, simple, and
effective methodology for extracting structured tabular data from textual
medical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of
LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately
inferring tabular features, even when their names are not explicitly mentioned
in the text. This is achieved by combining domain-specific reasoning guidelines
with a proposed data validation and reasoning correction feedback loop. By
applying interpretable ML models such as decision trees and logistic regression
over the extracted and validated data, we obtain end-to-end interpretable
predictions. We demonstrate that our approach significantly outperforms
state-of-the-art text classification models in medical diagnostics. Given its
predictive performance, simplicity, and interpretability, TEMED-LLM underscores
the potential of leveraging LLMs to improve the performance and trustworthiness
of ML models in medical applications.
