Depression Severity

Research on depression severity focuses on developing objective methods for assessing and predicting its levels, moving beyond traditional self-report measures. Current approaches leverage natural language processing (NLP) techniques, employing deep learning models like BERT, RoBERTa, Longformer, and LSTMs, analyzing diverse data sources including social media posts, speech recordings, and text messages to extract linguistic and prosodic features indicative of depression severity. These advancements offer the potential for improved early detection, personalized treatment strategies, and more efficient monitoring of treatment response, ultimately enhancing mental healthcare delivery.

Papers

October 23, 2024

Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches
Kexin Feng, Theodora Chaspari
Speech Analysis Ensemble Learning Depression Detection Speech Production Depression Recognition Depression Severity

October 16, 2024

Leveraging LLMs for Translating and Classifying Mental Health Data
Konstantinos Skianis, A. Seza Doğruöz, John Pavlopoulos
Large Language Model Mental Health Mental Health Care Depression Severity Linguistic Marker

January 25, 2024

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda
Richard Kimera, Daniela N. Rim, Joseph Kirabira, Ubong Godwin Udomah, Heeyoul Choi
Mental Health Gradient Boosting Labeling Technique Depression Severity Beck Depression Inventory Longformer Model

October 17, 2023

MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts
Fardin Ahsan Sakib, Ahnaf Atef Choudhury, Ozlem Uzuner
Deep Learning Social Medium Depression Symptom Damo NLP Depression Severity Beck Depression Inventory

October 5, 2023

Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity
Xinyang Ren, Hannah A Burkhardt, Patricia A Areán, Thomas D Hull, Trevor Cohen
Human Prediction Contextual Representation Deep Representation First Person Depression Severity

August 22, 2023

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Yuezhou Zhang, Amos A Folarin, Judith Dineley, Pauline Conde, Valeria de Angel, Shaoxiong Sun, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Petroula Laiou, Heet Sankesara, Linglong Qian, Faith Matcham, Katie M White, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Björn W. Schuller, Srinivasan Vairavan, Til Wykes, Josep Maria Haro, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, Nicholas Cummins, RADAR-CNS consortium
Speech Recognition System Linguistic Feature Large Scale Speech Depression Severity

August 21, 2023

What's Race Got to do with it? Predicting Youth Depression Across Racial Groups Using Machine and Deep Learning
Nathan Zhong, Nikhil Yadav
Deep Learning Machine Learning Classification Code New Machine Depression Symptom Depression Severity

July 17, 2023

A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale
Sijin Cai, Nanfeng Zhang, Jiaying Zhu, Yanjie Liu, Yongjin Zhou
System Performance GPT Neo Generative Pre Trained Transformer Beck Depression Inventory Depression Severity Depression Dataset

July 5, 2023

Using Random Effects Machine Learning Algorithms to Identify Vulnerability to Depression
Runa Bhaumik, Jonathan Stange
Random Forest Depression Symptom Latent Vulnerability Depression Severity Depression Risk Forest Classifier Depression Forecasting

January 24, 2023

Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity
Robert A. Lewis, Asma Ghandeharioun, Szymon Fedor, Paola Pedrelli, Rosalind Picard, David Mischoulon
Random Forest Mental Health Personalized Prediction Standard Random Forest Depression Severity Beck Depression Inventory

November 14, 2022

Semantic Similarity Models for Depression Severity Estimation
Anxo Pérez, Neha Warikoo, Kexin Wang, Javier Parapar, Iryna Gurevych
New Benchmark Social Medium Semantic Similarity Depression Recognition Depression Severity Beck Depression Inventory

October 10, 2022

DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mohsinul Kabir, Tasnim Ahmed, Md. Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, Kamrul Hasan
Social Medium Twitter Tweet Tweet Text Typological Exploration Depression Severity

September 8, 2022

Dyadic Interaction Assessment from Free-living Audio for Depression Severity Assessment
Bishal Lamichhane, Nidal Moukaddam, Ankit B. Patel, Ashutosh Sabharwal
Audio Driven Dyadic Interaction Depression Severity Clinical Interview

February 25, 2022

Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN
Kaining Mao, Wei Zhang, Deborah Baofeng Wang, Ang Li, Rongqi Jiao, Yanhui Zhu, Bin Wu, Tiansheng Zheng, Lei Qian, Wei Lyu, Minjie Ye, Jie Chen
Human Prediction Prosodic Feature Depression Detection Semantic Feature Multimodal Attention Bidirectional LSTM Depression Severity

December 27, 2021

Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment
Matthew Matero, Albert Hung, H. Andrew Schwartz
Pre Trained NLP Field Contextual Embeddings Layer Segmentation Depression Severity Mental Health Assessment

Depression Severity

Papers

Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches

Leveraging LLMs for Translating and Classifying Mental Health Data

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts

Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

What's Race Got to do with it? Predicting Youth Depression Across Racial Groups Using Machine and Deep Learning

A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale

Using Random Effects Machine Learning Algorithms to Identify Vulnerability to Depression

Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity

Semantic Similarity Models for Depression Severity Estimation

DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities

Dyadic Interaction Assessment from Free-living Audio for Depression Severity Assessment

Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN

Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment