Tıp odaklı dil modellerini ayırıcı tanı için geliştirmek: yapay veriden gerçek dünya değerlendirmesine doğru

Access to large-scale, annotated Electronic Health Records (EHR) is limited by privacy rules. This creates a major setback for training strong clinical natural language processing (NLP) models. Synthetic data provides a way to protect privacy, but how well synthetic text works for fine-tuning large language models (LLMs) in real-world tasks is still an important issue to explore. Our thesis presents a framework that uses synthetic patient summaries to fine-tune a medical LLM model for multi-label disease diagnosis. This approach offers a cost-effective and privacy-focused method for creating clinical diagnostic tools with minimal use of sensitive real-world data. The results show that synthetic data can successfully reshape the medical models. This also helps the hospitals that are struggling with triage and the overcrowding of patients.