The vast amount of available medical records has the potential to improve healthcare and biomedical research. However, privacy restrictions make these data accessible for internal use only. Recent works have addressed this problem by generating synthetic data using Causal Language Modeling. Unfortunately, by taking this approach, it is often impossible to guarantee patient privacy while offering the ability to control the diversity of generations without increasing the cost of generating such data. In contrast, we present a system for generating synthetic free-text medical records using Masked Language Modeling. The system preserves critical medical information while introducing diversity in the generations and minimising re-identification risk. The system's size is about 120M parameters, minimising inference cost. The results demonstrate high-quality synthetic data with a HIPAA-compliant PHI recall rate of 96% and a re-identification risk of 3.5%. Moreover, downstream evaluations show that the generated data can effectively train a model with performance comparable to real data.

翻译：海量的可用医疗记录具有改善医疗保健和生物医学研究的潜力。然而，隐私限制使得这些数据仅限于内部使用。近期研究尝试通过使用因果语言建模生成合成数据来解决此问题。遗憾的是，采用这种方法通常无法在保证患者隐私的同时，提供控制生成多样性的能力，且不增加数据生成成本。相比之下，我们提出了一种使用掩码语言建模生成合成自由文本医疗记录的系统。该系统在保留关键医疗信息的同时，引入了生成多样性并最小化重识别风险。系统规模约为1.2亿参数，最小化了推理成本。结果表明，所生成的合成数据质量高，其受保护健康信息（PHI）召回率符合HIPAA标准，达到96%，重识别风险为3.5%。此外，下游评估表明，生成的数据能够有效训练模型，其性能可与真实数据训练的模型相媲美。