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 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 medication prescriptions based on provided labels, such as a vocabulary list of medications and their attributes. 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 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}

翻译：获取真实的药物治疗处方对医学研究和医疗质量改进至关重要。然而，由于处方信息具有敏感性，真实处方的获取往往受到限制。此外，为训练和微调自然语言处理（NLP）模型而手动标注这些指令既繁琐又成本高昂。我们提出了一种新颖的任务特定模型架构——标签到文本Transformer（\textbf{LT3}），该架构能够根据提供的标签（例如药物及其属性词汇表）生成合成药物治疗处方。LT3基于从MIMIC-III数据库中提取的约2000条药物治疗处方进行训练，使其能够生成有价值的合成处方。我们通过将LT3与当前最先进的预训练语言模型（PLM）T5进行对比，分析生成文本的质量与多样性，从而评估其性能。我们将生成的合成数据用于训练SpacyNER模型，在n2c2-2018数据集上执行命名实体识别（NER）任务。实验表明，基于合成数据训练的模型在药物、频率、给药途径、剂量强度和剂型的标签识别任务中，F1分数可达96-98%。LT3的代码与数据将在\url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}共享。