Exploring Self-supervised Learning for Detecting Mental Disorders

Основные понятия

Investigating task-agnostic representations through self-supervised learning for detecting major depressive disorder (MDD) and post-traumatic stress disorder (PTSD).

Аннотация

This study explores the use of self-supervised learning models, PASE and AALBERT, to generate task-agnostic representations for detecting MDD and PTSD. The investigation is conducted using audio and video data collected during interactive sessions. The study focuses on modifying hyperparameters to improve detection performances for mental disorders.
I. Introduction

Studies focus on automatic detection of mental disorders using recorded interactions.
Challenges in finding appropriate feature representation from audio/video data.
Exploration of deep-learning architectures for generating suitable latent representations.
II. SSL Models
A. Multi-target prediction:

PASE architecture generates task-agnostic representation from raw speech.
Modified list of workers includes eGeMAPS, MFB energies, and LPS.
B. Masked prediction:
AALBERT architecture utilizes transformer layers to predict masked frames.
III. Experimental Details
A. Datasets:

Utilization of DAIC-WOZ dataset for developing MDD/PTSD detector.
B. Encoders and Detectors:

PASE/PASE-mod encoder trained on LibriSpeech, DAIC-WOZ, and IEMOCAP datasets.
AALBERT encoder trained on video modality with different input segment lengths.
C. Baselines:

Evaluation of LSTM models as baselines for comparison.
IV. Results
A. Audio Modality:

Detection performances of MDD and PTSD using PASE/PASE-mod encoders.
B. Video Modality:
Detection performances of MDD and PTSD using the AALBERT encoder.
V. Conclusions
The study investigates the task-agnostic traits of SSL representations for detecting correlated mental disorders in audio and video modalities, showing promising results in improving detection performances compared to supervised learning models.

Статистика

None

Цитаты

None

Ключевые выводы из

Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders

by Rohan Kumar ... в arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15170.pdf

Дополнительные вопросы

How can the findings from this study be applied to other mental health conditions?

The findings from this study, which explore the task-agnostic traits of representations derived through SSL for detecting correlated mental disorders, can be extrapolated to other mental health conditions with overlapping symptoms. By leveraging SSL models trained on diverse datasets and generating global representations that capture varying temporal contexts, researchers and practitioners can potentially apply similar methodologies to detect and diagnose a wide range of mental health conditions. This approach could be particularly useful for disorders with shared or similar behavioral manifestations, allowing for more accurate and efficient diagnostic processes.

What are the potential limitations or biases introduced by utilizing self-supervised learning models?

While self-supervised learning (SSL) models offer several advantages in generating task-agnostic representations across different domains, they also come with certain limitations and biases. One potential limitation is the quality of pseudo labels used in training these models. If the pseudo labels do not accurately represent the underlying data distribution or if they introduce noise or bias into the learning process, it can impact the effectiveness of SSL models. Additionally, SSL models may struggle with capturing complex relationships within high-dimensional data due to their unsupervised nature.
Another challenge is related to dataset selection and preprocessing. Biases present in training data can propagate through SSL models, leading to biased representations or inaccurate predictions. Moreover, hyperparameter tuning in SSL models can also introduce biases if not carefully optimized for specific tasks or datasets.
Furthermore, interpretability issues may arise when using SSL models for mental health diagnostics. The black-box nature of some deep learning architectures employed in SSL could make it challenging to understand how decisions are made by these models, raising concerns about transparency and trustworthiness in clinical settings.

How might advancements in SSL impact the future of mental health diagnostics?

Advancements in self-supervised learning (SSL) have significant implications for the future of mental health diagnostics by offering innovative approaches to feature representation learning from audiovisual data collected during interactive sessions between individuals and virtual interviewers/computer agents.

Improved Diagnostic Accuracy: By leveraging task-agnostic traits learned through advanced SSL techniques such as multi-target prediction or masked prediction-based architectures like PASE-mod and AALBERT explored in this study, clinicians may achieve higher accuracy rates when detecting various mental disorders based on behavioral cues extracted from audiovisual interactions.

Enhanced Generalization Across Disorders: The ability of SSL models to generate global representations capturing diverse temporal contexts enables better generalization across multiple correlated mental disorders that share common symptoms.

Efficient Data Utilization: With advancements in self-supervised representation learning techniques optimizing feature extraction without requiring extensive labeled datasets upfront—especially beneficial given limited availability—mental health professionals could streamline diagnostic processes while maintaining high standards of accuracy.

Personalized Treatment Plans: As SSL continues evolving towards more nuanced understanding of individual behaviors captured through audiovisual modalities during assessments, personalized treatment plans tailored specifically to each patient's needs could become more accessible based on comprehensive insights provided by sophisticated AI algorithms trained via self-supervision methods.

These advancements hold promise for revolutionizing how mental health conditions are diagnosed and treated by integrating cutting-edge technologies into traditional assessment practices while prioritizing patient-centric care delivery strategies supported by robust computational frameworks powered by state-of-the-art machine-learning innovations like self-supervised learning methodologies.