Generating Medical Prescriptions with Conditional Transformer

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}

Generating Medical Instructions with Conditional Transformer

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}

Large Language Models and Control Mechanisms Improve Text Readability of Biomedical Abstracts

Biomedical literature often uses complex language and inaccessible professional terminologies. That is why simplification plays an important role in improving public health literacy. Applying Natural Language Processing (NLP) models to automate such tasks allows for quick and direct accessibility for lay readers. In this work, we investigate the ability of state-of-the-art large language models (LLMs) on the task of biomedical abstract simplification, using the publicly available dataset for plain language adaptation of biomedical abstracts (\textbf{PLABA}). The methods applied include domain fine-tuning and prompt-based learning (PBL) on: 1) Encoder-decoder models (T5, SciFive, and BART), 2) Decoder-only GPT models (GPT-3.5 and GPT-4) from OpenAI and BioGPT, and 3) Control-token mechanisms on BART-based models. We used a range of automatic evaluation metrics, including BLEU, ROUGE, SARI, and BERTscore, and also conducted human evaluations. BART-Large with Control Token (BART-L-w-CT) mechanisms reported the highest SARI score of 46.54 and T5-base reported the highest BERTscore 72.62. In human evaluation, BART-L-w-CTs achieved a better simplicity score over T5-Base (2.9 vs. 2.2), while T5-Base achieved a better meaning preservation score over BART-L-w-CTs (3.1 vs. 2.6). We also categorised the system outputs with examples, hoping this will shed some light for future research on this task. Our code, fine-tuned models, and data splits are available at \url{https://github.com/HECTA-UoM/PLABA-MU}

Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data

Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challenging task for both resampling and GAN-based approaches. In this work we propose CA-GAN architecture that addresses some of the shortcomings of the current approaches, where we provide a detailed comparison with both SMOTE and WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343 hypotensive Caucasian and Black patients. We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.