DepressLLM: Depression Assessment Pipelines

• DepressLLM is a term for multiple LLM pipelines that transform unstructured depression-related evidence into structured symptom and risk representations.
• Each variant uses methodologies like zero-shot and few-shot prompting to extract clinical signals from diverse sources such as pediatric EHRs, interviews, and social media.
• Empirical evaluations demonstrate that these systems improve screening efficiency and support clinician triage without making autonomous clinical diagnoses.

DepressLLM is a label used in recent literature for several large-language-model systems aimed at depression-related assessment rather than a single fixed architecture. The name has been applied to a zero-shot pipeline for extracting depressive symptoms from pediatric electronic health record notes, to a depression detection and analysis framework on E-DAIC interviews, to a spoken-language PHQ-8 prediction system with psychological knowledge injection, and to a zero-shot Reddit depression-risk assessment system based on multi-label emotion recognition and a weighted severity index (Ignashina et al., 29 Jan 2025, Tank et al., 2024, Li et al., 28 May 2025, Gulino et al., 21 Apr 2026). Across these variants, the recurring objective is to transform unstructured notes, posts, transcripts, or speech into structured symptom, risk, or severity representations that are interpretable enough for screening, triage, or clinician support.

1. Nomenclature and scope

Explicit uses of the name in the current literature span multiple modalities and tasks (Ignashina et al., 29 Jan 2025, Tank et al., 2024, Li et al., 28 May 2025, Gulino et al., 21 Apr 2026).

Instantiation	Data source	Primary output
Pediatric EHR DepressLLM	Pediatric clinical notes, ages 6–24	16 note-level binary symptom labels
E-DAIC DepressLLM	E-DAIC / AVEC 2019 interviews	PHQ-8 regression and 3-class classification
Spoken-language DepressLLM	DAIC-WOZ audio and transcripts	PHQ-8 score regression
Reddit DepressLLM	DepressionEmo and large Reddit corpora	Eight emotion labels and severity score

This suggests that “DepressLLM” has functioned as a recurring research label for clinically or psychologically oriented LLM pipelines, not as a single canonical benchmark or model family. What unifies these systems is less their implementation than their representational strategy: each uses an LLM to impose structure on otherwise heterogeneous evidence. In one case that structure is a 16-dimensional symptom inventory, in another an eight-label depressive-emotion profile, and in another a PHQ-8 severity estimate derived from speech and transcript cues.

A second common feature is that the named systems are usually framed as assistive rather than fully autonomous. The pediatric variant is explicit that it is extracting evidence rather than making an automated diagnosis, and the Reddit risk system is presented as screening or triage rather than clinical diagnosis. This boundary condition is central to how the term has been operationalized in the literature.

2. Pediatric EHR symptom extraction

The most clinically explicit DepressLLM is the pediatric EHR system introduced for extracting depressive symptoms from free-text notes in pediatric primary care (Ignashina et al., 29 Jan 2025). Its motivation is the practical difficulty of depression screening in youth: PHQ-9 completion was patchy, full PHQ-9 documentation was especially inconsistent, and there was a large drop-off between initial screening and completion of all items. The framework therefore treats clinical notes as a richer source of evidence and formulates the task as binary symptom extraction at the note level for patients aged 6–24.

Ground truth was created by manually annotating 85 clinical notes from 22 patients. Sentence-level marking was collapsed into note-level binary labels, so a note became positive for a symptom if any sentence in that note contained evidence for it. The symptom inventory was built primarily from Beck’s Depression Inventory, augmented with PHQ-9-related concepts, and comprised 16 categories: not going to school, neglecting activities, no motivation, feeling depressed, feeling anxious, feeling down, irritability, mental health concerns, sleep problems, high appetite, low appetite, weight change, little energy, self-loathing, abnormal behavior, and suicidal thoughts.

Three locally run open-source models were evaluated: Flan-T5-small, Phi-3.5-mini-instruct, and quantized Llama 3-70B-Instruct. Flan used a zero-shot entailment-style prompt, phrased as “Does the premise entail the hypothesis?”, whereas Phi and Llama used few-shot prompts with three in-context examples. For Phi and Llama, notes were truncated to 6,000 characters because of token limits, causing truncation in 18% of notes. A rigid word-match baseline matched keywords from each symptom query.

Performance was reported with precision, recall, and F1 for the positive class. All LLMs outperformed the word-match baseline, whose average F1 was around 0.22. Flan-T5 was best overall, with average precision 0.78, recall 0.55, and F1 0.65; Phi was more balanced but lower overall at roughly precision 0.44, recall 0.60, and F1 0.51; Llama 3 had the highest recall at 0.90 but low precision at 0.33 and F1 at 0.48, reflecting systematic overgeneralization. The paper also states that the LLMs were about 60% more efficient than word matching, with Flan the most computationally attractive option among them.

Symptom-level results clarify the extraction tradeoff. Flan achieved perfect precision on categories such as not going to school, neglecting activities, no motivation, feeling depressed, irritability, sleep problems, high appetite, and abnormal behavior. Its strongest F1 values included sleep problems at 0.92 and self-loathing at 0.80. Phi performed relatively well on broader emotional and somatic categories such as feeling depressed at 0.69 and weight change at 0.78. Llama 3 reached very high recall, including perfect recall on some symptoms, but often inferred unsupported symptoms from generic depression language.

The study also tested whether extracted symptoms are useful beyond extraction itself. In a downstream case-control experiment on the 15–17-year-old cohort, the authors assembled 3,000 notes near PHQ-9 completion for 462 cases and matched 462 controls by age and gender, yielding 924 patients. Flan detections were aggregated into a 16-dimensional symptom vector for each patient, with each dimension defined as the proportion of notes containing that symptom. These vectors were fed to Random Forest, Logistic Regression, SVM, Multi-layer Perceptron, and Decision Tree classifiers. The best model was SVM, with AUC-ROC 0.71, F1 0.63, precision 0.78, and recall 0.53, versus a BigBird baseline on raw merged notes that reached AUC-ROC 0.60, F1 0.45, precision 0.43, and recall 0.55. The paper summarizes this as about an 8% absolute improvement in AUC-ROC.

3. Social-media risk scoring and explanation

A separate DepressLLM line addresses depression risk assessment in Reddit text through zero-shot multi-label emotion recognition (Gulino et al., 21 Apr 2026). Instead of predicting a single class, it labels each post for eight depression-associated emotions: anger, cognitive dysfunction, emptiness, hopelessness, loneliness, sadness, suicide intent, and worthlessness. The prompt forces machine-readable JSON output with True/False decisions for each label and can also request a severity_score.

Risk is computed with a weighted depressive severity index:

$$S = 1 \cdot \text{anger} + 1 \cdot \text{cog\_dysfunction} + 1 \cdot \text{emptiness} + 2 \cdot \text{hopelessness} + 1 \cdot \text{loneliness} + 1 \cdot \text{sadness} + 3 \cdot \text{suicide\_intent} + 2 \cdot \text{worthlessness}.$$

The thresholds are $S=0\text{–}1$ for minimal/absent depression, $S=2\text{–}4$ for mild depression, $S=5\text{–}6$ for moderate depression, and $S \ge 7$ for severe depression / high alert. On the annotated DepressionEmo dataset of about 6,000 Reddit posts, the best model, gemma3:27b, achieved micro-F1 0.75 and macro-F1 0.70, compared with a fine-tuned BART baseline at 0.80 and 0.76. The same model was then applied to 469,692 posts from r/anxiety, r/depression, r/depression_partners, and r/mentalhealth collected from January 2024 to May 2025. In that in-the-wild analysis, r/depression had a mean risk score of approximately 7 and around 43% of posts with $S \ge 7$, whereas r/anxiety had median approximately 2 and only about 3% of posts with $S \ge 7$.

Related social-media work has extended the same representational logic toward explanation generation. LLM-MTD jointly optimizes depression classification and medically informed explanation generation on the Reddit Self-Reported Depression Dataset with the combined loss

$$\mathcal{L}_{total} = \lambda \mathcal{L}_{cls} + (1 - \lambda)\mathcal{L}_{gen},$$

and reports 0.882 accuracy, 0.820 F1, and 0.880 AUPRC; in a human study of 100 correctly classified positive posts, its explanations were rated 4.5 for relevance, 4.2 for completeness, and 4.6 for medical accuracy on a 1–5 scale (Chen et al., 18 Mar 2025).

A further step toward evaluation standardization is an expert-annotated dataset of 1,017 social-media posts with depressive spans and 12 DSM-