The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach.

翻译：数字健康数据的迅猛增长引发了利用自然语言处理（NLP）等机器学习方法分析医疗记录、临床笔记及其他基于文本的健康信息的广泛兴趣。尽管NLP技术在提升患者护理和辅助临床决策方面展现出巨大潜力，但数据隐私和法规遵从性仍是关键问题。联邦学习（FL）作为一种可行方案应运而生，它使多个机构能够在不共享原始数据的情况下协同训练机器学习模型。本文提出了一种结合FL、NLP模型及NVIDIA开发的NVFlare框架的实用医疗NLP方法。我们介绍了两种优秀的NLP模型——基于长短期记忆（LSTM）的模型和双向编码器表示转换器（BERT），它们在理解医疗数据中的上下文和语义方面表现出色。本文涵盖了一个集成框架的开发，该框架在保持高精度和高性能的同时，解决了数据隐私和法规遵从性挑战，并包含BERT预训练，全面验证了所提出方法的有效性。