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Related Experiment Video

Updated: Aug 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Automatic depression score estimation with word embedding models.

Anxo Pérez1, Javier Parapar1, Álvaro Barreiro1

  • 1Information Retrieval Lab, CITIC, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.

Artificial Intelligence in Medicine
|October 7, 2022
PubMed
Summary

This study introduces a new method using neural language models to automatically estimate depression severity from social media posts. This approach offers a feasible alternative for early detection, even with limited data.

Keywords:
Depression predictionNeural language modelsSocial mediaWord embeddings

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Artificial intelligence in healthcare

Background:

  • Depression is a prevalent mental health disorder, yet early and efficient detection remains challenging.
  • Traditional diagnostic methods rely on self-report questionnaires, which can be influenced by social stigma.
  • Automated approaches are needed to overcome limitations of current diagnostic tools.

Purpose of the Study:

  • To develop and evaluate novel methods for automatically estimating depression severity using social media data.
  • To explore the effectiveness of neural language models in capturing depression symptoms from user writings.
  • To address the eRisk 2020 task of Measuring the Severity of the Signs of Depression.

Main Methods:

  • Two distinct neural language model-based methods were developed: one analyzing general user language and another focusing on posts mentioning specific depression symptoms.
  • Both methods aimed to automatically estimate the Beck Depression Inventory (BDI-II) total score.
  • Benchmark Reddit data was utilized for evaluating the proposed methods.

Main Results:

  • Neural language models proved to be a feasible alternative for estimating depression severity.
  • The proposed methods demonstrated effectiveness even with limited training data.
  • The study successfully addressed the challenge of automatic depression severity estimation from social media.

Conclusions:

  • Automated estimation of depression severity from social media using neural language models is a viable approach.
  • This technology can potentially aid in the early detection of depression, complementing traditional methods.
  • Further research can refine these models for broader clinical application.