The increasing demand for synthetic clinical data has been driven by strict privacy constraints on real-world electronic health records (EHRs) and the need for annotated data, particularly for rare conditions. DualAlign introduces a solution by using statistical and semantic alignment techniques, ensuring that the generated data reflects real-world patient demographics and symptom trajectories. This approach is particularly beneficial in the context of Alzheimer’s disease, where traditional datasets may be scarce.
Despite its advantages, DualAlign has limitations in capturing the full complexity of longitudinal clinical scenarios. However, it significantly improves the quality of synthetic data available for low-resource clinical text analysis. By fine-tuning a large language model with both DualAlign-generated content and human-annotated data, researchers can achieve better outcomes compared to relying solely on high-quality or unguided synthetic datasets. The implications of this work could transform how clinical researchers utilize synthetic data, enhancing both privacy and the quality of analysis.
👉 Pročitaj original: arXiv AI Papers
