Machine Learning / Translational Barriers
We explore challenges in applying machine learning to neuroscience and psychiatry, focusing on explainability, data noise, confounders, and generalizability to enhance clinical applications.
Translational Barriers
We investigate the challenges of applying machine learning to neuroscience and precision psychiatry. We focus on issues such as model explainability, data noise, confounders, and cross-sample generalizability. Our aim is to identify and address the roadblocks that hinder the translation of promising machine learning techniques into practical clinical applications. Through our work, we contribute to the ongoing dialogue about the realistic potential and limitations of machine learning in brain research and personalized psychiatric care.
