mlsanityData to 14 Oct 2026

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

arXiv 2204.10432 ↗First posted 21 Apr 2022cs.CLcited by 27

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Abstract (arXiv)

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

14 of 43 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  8. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 234
  9. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 84
  10. 2018SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditionscited by 73
  11. 2019Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classificationcited by 63
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2 more
  1. 2020Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Frameworkcited by 25
  2. 2021Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Healthcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 9; 2024: 7; 2025: 3; 2026: 4.

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  1. 2023Can Large Language Models Transform Computational Social Science?cited by 250
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 98
  3. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  4. 2023MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Modelscited by 63
  5. 2023Towards Interpretable Mental Health Analysis with Large Language Modelscited by 53
  6. 2022Symptom Identification for Interpretable Detection of Multiple Mental Disorderscited by 20
  7. 2024Depression Detection on Social Media with Large Language Modelscited by 20
  8. 2024Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosiscited by 12
  9. 2024From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?cited by 11
  10. 2023Explainable Depression Symptom Detection in Social Mediacited by 9
  11. 2023A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depressioncited by 8
  12. 2024A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Healthcited by 7
15 more
  1. 2022Semantic Similarity Models for Depression Severity Estimationcited by 6
  2. 2023Exploring Social Media for Early Detection of Depression in COVID-19 Patientscited by 6
  3. 2024Can Large Language Models be Used to Provide Psychological Counselling? An Analysis of GPT-4-Generated Responses Using Role-play Dialoguescited by 6
  4. 2024On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlookcited by 6
  5. 2023Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Mediacited by 5
  6. 2025Datasets for Depression Modeling in Social Media: An Overviewcited by 5
  7. 2023A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnairescited by 4
  8. 2026Responsible Evaluation of AI for Mental Healthcited by 2
  9. 2024They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Modelscited by 1
  10. 2025Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarizationcited by 1
  11. 2022Deep Temporal Modelling of Clinical Depression through Social Media Textcited by 0
  12. 2025AI Models for Depressive Disorder Detection and Diagnosis: A Reviewcited by 0
  13. 2026WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detectioncited by 0
  14. 2026Beyond Reflection: Affirmation as a Promising Behavioral Marker Associated with Quality in Text-Based Counselingcited by 0
  15. 2026PAT: A Personality-Driven Augmentation and Transfer Learning Framework for Depression Detection on Social Mediacited by 0