LT3: Generating Medication Prescriptions with Conditional Transformer

Abstract

Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement.However, access to real medication prescriptions is often limited due to the sensitive nature of the information expressed.Additionally, manually labelling these instructions for training and finetuning Natural Language Processing (NLP) models can be tedious and expensive.We introduce a novel task-specific model architecture, Label-To-Text-Transformer (LT3), tailored to generate synthetic medication prescriptions based on provided labels, such as a vocabulary list of medications and their attributes, to facilitate safe healthcare research.LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions.We evaluate LT3's performance by contrasting it with state-of-the-art Pre-trained Language Models (PLMs), T5-small/base/large, 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.

Authors 5

1. Samuel Belkadi

   University of Cambridge

   Affiliation as printed

   Cambridge University , UK

2. Nicolo Micheletti

   Tsinghua University

   Affiliation as printed

   Tsinghua University , China

3. Lifeng Han Aachen LIACS & LUMC

   Leiden University · Leiden University Medical Center · University of Manchester

   Affiliation as printed

   LIACS & LUMC , Leiden University , NL

   The University of Manchester , UK

4. Warren Del-Pinto

   University of Manchester

   Affiliation as printed

   The University of Manchester , UK

5. Goran Nenadić

   University of Manchester

   Affiliation as printed

   The University of Manchester , UK