# Deep Knowledge-Infusion For Explainable Depression Detection arXiv: https://arxiv.org/abs/2409.02122 ยท HTML: https://mlsanity.com/paper/2409.02122/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 61 references that are in the map. - First posted on arXiv 1 Sep 2024. ## Builds on (most cited first, 29 of 29) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019) - [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013) - [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021) - [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020) - [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021) - [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021) - [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022) - [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018) - [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021) - [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022) - [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017) - [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022) - [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021) - [UCPhrase: Unsupervised Context-aware Quality Phrase Tagging](https://mlsanity.com/paper/2105.14078/) (2021) - [Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://mlsanity.com/paper/2003.07634/) (2020) - [Learning Models for Suicide Prediction from Social Media Posts](https://mlsanity.com/paper/2105.03315/) (2021) - [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023) - [t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams](https://mlsanity.com/paper/1911.06147/) (2019) - [Lexicon-based Methods vs. BERT for Text Sentiment Analysis](https://mlsanity.com/paper/2111.10097/) (2021) - [Detecting and Explaining Crisis](https://mlsanity.com/paper/1705.09585/) (2017) - [Cost-sensitive Boosting Pruning Trees for depression detection on Twitter](https://mlsanity.com/paper/1906.00398/) (2019) - [Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media](https://mlsanity.com/paper/2007.02847/) (2020) - [Security and Privacy-Preservation of IoT Data in Cloud-Fog Computing Environment](https://mlsanity.com/paper/2212.00321/) (2022) - [Does BERT look at sentiment lexicon?](https://mlsanity.com/paper/2111.10100/) (2021) ## Built on by (most cited first, 2 of 2) - [Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media](https://mlsanity.com/paper/2503.01082/) (2025) - [When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening](https://mlsanity.com/paper/2605.23148/) (2026) ## Abstract (arXiv) Discovering individuals depression on social media has become increasingly important. Researchers employed ML/DL or lexicon-based methods for automated depression detection. Lexicon based methods, explainable and easy to implement, match words from user posts in a depression dictionary without considering contexts. While the DL models can leverage contextual information, their black-box nature limits their adoption in the domain. Though surrogate models like LIME and SHAP can produce explanations for DL models, the explanations are suitable for the developer and of limited use to the end user. We propose a Knolwedge-infused Neural Network (KiNN) incorporating domain-specific knowledge from DepressionFeature ontology (DFO) in a neural network to endow the model with user-level explainability regarding concepts and processes the clinician understands. Further, commonsense knowledge from the Commonsense Transformer (COMET) trained on ATOMIC is also infused to consider the generic emotional aspects of user posts in depression detection. The model is evaluated on three expertly curated datasets related to depression. We observed the model to have a statistically significant (p<0.1) boost in performance over the best domain-specific model, MentalBERT, across CLEF e-Risk (25% MCC increase, 12% F1 increase). A similar trend is observed across the PRIMATE dataset, where the proposed model performed better than MentalBERT (2.5% MCC increase, 19% F1 increase). The observations confirm the generated explanations to be informative for MHPs compared to post hoc model explanations. Results demonstrated that the user-level explainability of KiNN also surpasses the performance of baseline models and can provide explanations where other baselines fall short. Infusing the domain and commonsense knowledge in KiNN enhances the ability of models like GPT-3.5 to generate application-relevant explanations.