Clinical Signature

Clinical signatures represent the unique patterns of data—be it imaging, vital signs, or electronic health records—that characterize specific diseases or disease subtypes. Current research focuses on developing unsupervised machine learning methods, including deep learning architectures like autoencoders and transformers, to identify these signatures from complex, high-dimensional datasets. This work aims to improve disease diagnosis, prognosis, and treatment selection by providing more precise and individualized assessments than traditional methods, ultimately leading to more effective and targeted healthcare interventions. The ability to uncover latent, clinically relevant patterns holds significant promise for advancing precision medicine across various diseases.