Predicting diagnosis on first-episode psychosis through fingerprints, genomics and deep learning algorithms
The general aim of our project is to contribute to the identification of biologically informed markers with direct applicability for early detection and diagnostic precision of psychotic disorders. Starting from a fingerprint-based algorithm that we have developed previously through machine learning methods, wem propose to combine biometric anf genetic markers to generate a multimodal biomarker with discriminant potential for psychotic disorders. Clinical interviews will remain central to the diagnosis of psychotic disorders, but the out come of this proposal could bring a new tool to help establish sooner a precise diagnosis, which is a major clinical need intrinsically related to improving the disorder treatment and prognosis.
CIBERSAM ISCIII - Intramural