Large language model to generate synthetic electronic medical records
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1275Keywords:
Open Science, Large Language Model, Electronic Health RecordsAbstract
Introduction: The use of health data in research is limited by ethical issues. This challenges researchers to find ways to obtain the necessary material to carry out their work. Method: A Large Language Model (LLM) tool was used to generate synthetic electronic health records (EHR) for cardiology patients, employing the techniques of "few-shot prompting" and "chain-of-thought prompting". Objectives: Create a comprehensive and accessible dataset to aid in training text classification algorithms in medical scenarios. Results: 103 synthetic EHR were generated, covering different cardiac diagnoses. Conclusion: The generation of synthetic EHR through LLM presented the expected quality, being consistent with the content found in real EHR. The dataset is available in the Zenodo repository for unrestricted use by the research community, following the concept of open science.
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