PIE: A Personalized Information Embedded model for text-based depression detection

Abstract

Depression detection based on text analysis has emerged as a research hotspot. Existing research indicates that patients’ personalized characteristics are the primary factor contributing to differences in reported experiences, which poses challenges for automated depression detection methods. To address this, we pioneered defining the fundamental components of personalized information within the text-based depression detection field and proposed the Personalized Information Embedding (PIE) model. The model narrows the gap between generic clinical symptoms and personalized patient experiences in detection, introducing a novel method for computing personalized information representations. Then, we constructed a unique depression intervention dataset containing 108 cases of subjects, the first longitudinally gathering experimental dataset in text-based depression detection. Extensive experimental evidence demonstrates that compared to advanced models, PIE demonstrates statistically significant improvements in performance (with the maximum reductions in RMSE of 0.309 and MAE of 0.232) and generalizability (with standard deviation reductions in RMSE by 75.43% and MAE by 69.77%), and the out-of-domain generalizability of personalized information representations has been validated on two larger external datasets. Additionally, we conducted case studies to analyze how personalized information leads to improved model capabilities. This research serves as a pilot and reference for developing personalized models in text-based depression detection.