I think the main issue is that most mental illnesses are vaguely defined clusters of problems - if you represented a diagnosis as a vector where each index corresponded to severity of a particular symptom, the diagnosis space would not properly aligned with the actual disease basis. This introduces ambiguity - a single disease tends to fall into multiple clusters when your representational domain is misaligned with the actual data axes.
The solution is ML. Neural networks with appropriate architectures effectively perform a change of basis, mapping the data basis to an output coordinate system. When properly trained they can automagically orient their internal representation with the true data bases.
The solution is ML. Neural networks with appropriate architectures effectively perform a change of basis, mapping the data basis to an output coordinate system. When properly trained they can automagically orient their internal representation with the true data bases.