MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Abstract

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.