A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)
<|reference_start|>A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs): Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and analysis. Recent advancements in Artificial Intelligence (AI), particularly the development of Large Language Models (LLMs), open up new opportunities for researchers in this domain. Although prior studies have demonstrated their potential in language understanding and processing in the context of EHRs, a comprehensive scoping review is lacking. This study aims to bridge this research gap by conducting a scoping review based on 329 related papers collected from OpenAlex. We first performed a bibliometric analysis to examine paper trends, model applications, and collaboration networks. Next, we manually reviewed and categorized each paper into one of the seven identified topics: named entity recognition, information extraction, text similarity, text summarization, text classification, dialogue system, and diagnosis and prediction. For each topic, we discussed the unique capabilities of LLMs, such as their ability to understand context, capture semantic relations, and generate human-like text. Finally, we highlighted several implications for researchers from the perspectives of data resources, prompt engineering, fine-tuning, performance measures, and ethical concerns. In conclusion, this study provides valuable insights into the potential of LLMs to transform EHR research and discusses their applications and ethical considerations.<|reference_end|>
@article{li2024a,
    title={A scoping review of using Large Language Models (LLMs) to investigate
  Electronic Health Records (EHRs)},
    author={Lingyao Li, Jiayan Zhou, Zhenxiang Gao, Wenyue Hua, Lizhou Fan, Huizi
  Yu, Loni Hagen, Yongfeng Zhang, Themistocles L. Assimes, Libby Hemphill,
  Siyuan Ma},
    journal={arXiv preprint arXiv:2405.03066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03066},
    primaryClass={cs.ET}
}