Journal of biomedical informatics · 2025
A LangChain-based pipeline for one-shot synthetic text generation using generative pre-trained transformers in palliative care research.
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Ronan Isabel, Crowley Patrice, Rombouts Eva, Cornally Nicola, Saab Mohamad M, Murphy David, Tabirca Sabin
Аннотация
OBJECTIVE: As the world's population ages, nursing homes are of increasing importance. In order to care for a growing number of older adults, intelligent technologies are needed. Artificial Intelligence can be utilised to enhance palliative care in nursing homes. However, the data needed to train artificially intelligent agents is lacking within this sensitive domain due to privacy issues. Therefore, it is difficult for researchers to develop technological solutions. With the advent of large language models, such as ChatGPT, new text generation methods are made possible using limited data. In this pilot study, we investigate the use of large language models to generate synthetic data.
METHODS: We investigate the feasibility of using GPT-3.5 and GPT-4o models along with one-shot prompting to produce synthetic nurse notes which faithfully describe nursing home residents with met or unmet palliative care needs. We used LangChain to create a repeatable pipeline which can be adapted to different use-cases. We also compare the performance of both models using a set of qualitative and quantitative evaluations to determine which set of notes is more suitable for subsequent research.
RESULTS: GPT-3.5 performed slightly better than GPT-4o in our qualitative healthcare professional analysis. Quantitative analysis revealed appropriately heterogenous results across contextual similarity, lexical overlap, sentiment, and readability scores.
CONCLUSION: Our work is the first investigation of such a generation method in the nursing home palliative care domain. Further refinement and validation of such data is needed in order to ensure the safe use of our approach.
METHODS: We investigate the feasibility of using GPT-3.5 and GPT-4o models along with one-shot prompting to produce synthetic nurse notes which faithfully describe nursing home residents with met or unmet palliative care needs. We used LangChain to create a repeatable pipeline which can be adapted to different use-cases. We also compare the performance of both models using a set of qualitative and quantitative evaluations to determine which set of notes is more suitable for subsequent research.
RESULTS: GPT-3.5 performed slightly better than GPT-4o in our qualitative healthcare professional analysis. Quantitative analysis revealed appropriately heterogenous results across contextual similarity, lexical overlap, sentiment, and readability scores.
CONCLUSION: Our work is the first investigation of such a generation method in the nursing home palliative care domain. Further refinement and validation of such data is needed in order to ensure the safe use of our approach.