A new medical framework uses 'yogic psychology' to predict mental health issues better than the usual doctor's checklist.
Instead of just asking patients if they feel anxious or sad, this system maps circadian rhythms, nutrition, and lifestyle into a digital knowledge graph. The study demonstrated that this ancient-inspired model can actually outperform modern clinical tools in identifying the underlying drivers of emotional distress.
Integrative, and Scalable mental health phenotyping using a knowledge-graph-derived dual-metric framework
medRxiv · 10.64898/2026.03.09.26347798
Prevailing diagnostic instruments for anxiety and depression, though clinically indispensable, remain anchored to symptom-focused queries that assess patients directly about their affective states, while often neglecting the multidimensional architecture of daily living. Here, we introduce two complementary metrics, the Cognitive Attention Score (CAS) and C:ERR (Cognition-to-Emotional-Response Ratio), derived from yogic psychology and operationalized within a structured knowledge graph (Ceekr-KG