Fig. 3. Transdiagnostic prediction.

Current theories postulate that symptoms lie on a continuum, where distinct symptoms group together in overlapping clusters. As a result, and as discussed in “Dirty data”, real-world patients often exhibit many different patterns of symptoms and comorbidities rather than a single distinct pattern. Such viewpoints make classification into textbook diagnoses difficult as these diagnoses are based on meeting exemplar symptom patterns. Predictive models offer a solution to transdiagnostic problems, either by placing an individual into a cluster of patients that most mimic their spectrum of symptoms (i.e., transdiagnostic clustering) or by identifying brain networks that predict symptoms and generalize across a spectrum of traditional clinical categories and “healthy” individuals (i.e., transdiagnostic regression).