Identifying Psychiatric Manifestations in Outpatients with Depression and Anxiety: A Large Language Model-Based Approach

PurposeAccurate psychiatric diagnosis and assessment are crucial for effective treatment. However, while current data-driven approaches emphasize diagnostic outcomes, the process of decoding the underlying symptom expressions in patients language and mapping them to well-defined psychiatric terminology has received relatively little attention. This study investigates the potential of Large Language Models (LLMs) to automate the identification of diagnostic categories and symptoms from psychiatrist-patient dialogues, to provide interpretable insights and support automatic diagnosis. MethodsWe analyzed audio recordings from 1160 psychiatric diagnostic interviews, primarily involving patients with depressive disorder and anxiety disorder. A clinical entities corpus was formed by leveraging clinical annotations in EMRs (e.g., chief complaints, mental status, elements in assessment scales) and widely used assessment scales. LLMs were utilized to identify clinical symptoms, rate assessment scales, and an ensemble learning pipeline was designed to classify diagnostic results and symptoms with 10-fold cross-validation. ResultsThe system achieved 86.9% accuracy for identifying the appearance of clinical annotations and 74.7% (77.2%) accuracy for identifying anxiety (depression) symptoms. Patients with depression and anxiety, diagnosed using ICD-10 codes, were differentiated with an accuracy of 75.5%. Analysis of LLM-generated features shows that depression cases exhibited prominent markers of anhedonia and decreased volition, whereas anxiety disorders were characterized by tension and an inability to relax. ConclusionThis study demonstrates the potential of integrating LLM technology with linguistic and acoustic features to enhance psychiatric diagnostics. The developed pipeline effectively predicts psychiatric diagnoses and provides interpretable insights, showcasing a valuable tool for clinicians in mental health assessment.