Generating Medical Instructions with Conditional Transformer
CoRR(2023)
摘要
Access to real-world medical instructions is essential for medical research
and healthcare quality improvement. However, access to real medical
instructions is often limited due to the sensitive nature of the information
expressed. Additionally, manually labelling these instructions for training and
fine-tuning Natural Language Processing (NLP) models can be tedious and
expensive. We introduce a novel task-specific model architecture,
Label-To-Text-Transformer (\textbf{LT3}), tailored to generate synthetic
medical instructions based on provided labels, such as a vocabulary list of
medications and their attributes. LT3 is trained on a vast corpus of medical
instructions extracted from the MIMIC-III database, allowing the model to
produce valuable synthetic medical instructions. We evaluate LT3's performance
by contrasting it with a state-of-the-art Pre-trained Language Model (PLM), T5,
analysing the quality and diversity of generated texts. We deploy the generated
synthetic data to train the SpacyNER model for the Named Entity Recognition
(NER) task over the n2c2-2018 dataset. The experiments show that the model
trained on synthetic data can achieve a 96-98\% F1 score at Label Recognition
on Drug, Frequency, Route, Strength, and Form. LT3 codes and data will be
shared at \url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}
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关键词
transformer,medical
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