Abstract
Abstract
Traditional categorical diagnoses often fail to capture the dimensional nature of psychiatric disorders such as depression and anxiety. Natural Language Processing (NLP) offers a promising approach to extract dimensional constructs relevant to psychopathology from clinical documentation. In this cross-sectional study, we analyzed clinical notes from 358 psychiatric patients treated at McLean Hospital and enrolled in the “Psychometrics at McLean” (PAM) study to examine the alignment between NLP-derived Research Domain Criteria (RDoC) constructs and psychometric assessments of depression, anhedonia, and anxiety. RDoC scores for Positive Valence Systems (PVS) and Negative Valence Systems (NVS) were extracted using NLP methods previously validated at the group level and compared with standardized clinical assessments: Positive Valence Systems Scale (PVSS), Quick Inventory of Depressive Symptomatology (QIDS), Snaith-Hamilton Pleasure Scale (SHAPS), and Generalized Anxiety Disorder-7 (GAD-7). Significant associations were observed between depression (QIDS) and both PVS (β = −0.42,
p
= 0.008) and NVS (β = 0.45,
p
= 0.015); anhedonia (SHAPS) and both PVS (β = −0.39,
p
= 0.005) and NVS (β = 0.36,
p
= 0.030); and anxiety (GAD-7) and NVS (β = 0.29,
p
= 0.049), but not anxiety and PVS. These results are consistent with theoretical accounts in which both PVS and NVS are implicated in depression, whereas anxiety primarily involves NVS. These findings demonstrate that NLP-derived RDoC scores align with established clinical measures, providing evidence of convergent validity with symptom-based measures and of discriminant validity, most notably the selective association of anxiety with NVS. NLP-derived RDoC scores may offer a scalable approach for characterizing dimensional psychopathology; however, longitudinal and predictive validation will be required before applications such as treatment-response monitoring can be supported.