AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-16
An automata-machine learning integrated framework for intelligent mental health diagnostics
Depression among students often goes undetected until it becomes serious, so early prediction is needed. The authors combined finite automata-based behavioural modelling with machine learning, generating features from students' emotional states and merging redundant features. Using an ensemble of random forest and KStar, accuracy rose from an 82.02% baseline with random forest to 96.88% with the automata-based features. The authors state that the approach keeps the features interpretable and traceable.