Sigmund is an expert-guided human/machine hybrid “mind” applied to mental condition assessments. Employing the diagnoses of many psychiatric experts, and digitizing diagnostic approaches from multiple perspectives, Sigmund has effectively combined collective knowledge to determine a low-bias and highly-informed predisposition and probability assessment. The approach employs machine-learning to calibrate/correlate and correlate mental condition ground truths with a purpose-built Reference Database of individual personal “signatures” to determine which combinations of biometric variables most accurately predict mental condition predispositions and probabilities. New incoming records can be compared to this matrix to predict high-accuracy, evidence-based predisposition/probability scores. York University’s Clinical Study has generated the following results (the following chart predicts depression probabilities using three different approaches):

1. The original “expert-guided” approach where the predictive scoring is guided by psychoanalytical experts in advance of the application of machine-learning; and
2. two “supervised” approaches where no expert guidance is provided in advanceof machine-learning application.

Approach comparison

The above Chart shows that the machine-learning results corroborate the expert-guided results. DNA addition increased precision to 90%. All approaches used the same psychiatric “ground truth” diagnoses targets.

Anxiety assessment results revealed that machine-learning methods significantly outperformed the expert-guided approach. Sigmund utilizes both methods to optimise its own performance.