- SYN-Y1-2024-001: Abstract [D'Amico et al., Humanitas Research Hospital]
- Blood, journal of the American Society of Hematology, November 2024. Conference: ASH2024, American Society for Hematology Annual Meeting. Open publication >
- This study, conducted within the GenoMed4All and Synthema consortia, presents an advanced framework for generating and validating high-fidelity multimodal synthetic data (SD) for patients with myeloid neoplasms. Using a combination of conditional GANs, VAEs, Tabular-GPT, a fine-tuned LLM for longitudinal data, and Stable Diffusion for bone marrow image generation, the authors produced synthetic datasets that mirror complex real-world clinical, genomic, transcriptomic, and morphological information. A dedicated Synthetic Validation Framework (SVF) demonstrated strong statistical, biological, and clinical fidelity across all data types, with fidelity metrics ranging from 87% to 96%. Synthetic transcriptomes preserved key molecular patterns and pathway enrichments, while longitudinal SD accurately reproduced overall and leukemia-free survival distributions. Privacy assessments confirmed low re-identification risk. The study further showed that SD can strengthen machine-learning applications: models trained on synthetic or hybrid (real + SD) datasets achieved comparable or improved performance in disease classification and prognostic prediction. A clinician-friendly platform, JUNO, was also developed to generate multimodal SD from biobank data. Overall, the findings demonstrate that generative AI can produce privacy-preserving, clinically meaningful multimodal synthetic datasets that enhance predictive modelling and have the potential to accelerate research and personalized medicine in hematology.
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