MentalHealthDaily

Student Mental Health AI Reached 95% Accuracy in Kaggle Data

by GLOOM

A 2026 PLOS One machine-learning study reported 95.0% accuracy for a hybrid FT-Transformer plus long short-term memory (LSTM) model predicting student mental-health risk. The technical result is strong inside the dataset, but the clinical claim is still limited because the labels came from repository data rather than prospective clinical diagnosis.1 Research Highlights 95.0% accuracy was …

XGBoost Suicide Risk Model Reached 96% PPV at Top 0.1% Threshold

by GLOOM

MHD featured image for suicide-risk machine learning, precision, cost, and fairness.

A 2026 Scientific Reports study of Maryland suicide-death records found that an XGBoost machine-learning model could reach 96.1% positive predictive value in hospital-discharge data at the top 0.1% risk threshold, but it still detected only 46.7% of suicide deaths in that cohort. Research Highlights Precision improved at the narrowest threshold: XGBoost reached PPV 0.961 in …