Summary of Deep Knowledge-infusion For Explainable Depression Detection, by Sumit Dalal et al.
Deep Knowledge-Infusion For Explainable Depression Detection
by Sumit Dalal, Sarika Jain, Mayank Dave
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for detecting depression on social media using Knowledge-infused Neural Network (KiNN). The model incorporates domain-specific knowledge from DepressionFeature ontology (DFO) and commonsense knowledge from Commonsense Transformer (COMET) trained on ATOMIC. This allows the KiNN to provide user-level explainability regarding concepts and processes understood by clinicians. The paper evaluates KiNN on three expertly curated datasets related to depression, demonstrating a statistically significant boost in performance over MentalBERT, a state-of-the-art domain-specific model. The results show that KiNN’s generated explanations are informative for mental health professionals (MHPs) and surpass the performance of baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand social media posts to detect depression. It uses special computer programs called Neural Networks, which are like super-smart calculators. These networks can learn from data and make decisions based on what they know. The new approach is designed to explain its thinking in a way that makes sense for people who work with mental health patients. By using this approach, the computer model can detect depression more accurately than previous models. This could help humans understand why someone might be feeling depressed based on their social media posts. |
Keywords
» Artificial intelligence » Neural network » Transformer
