Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment | JoVE Visualize

Area of Science:

Computational linguistics
Mental health informatics
Machine learning for healthcare

Background:

Internet-Delivered Psychological Treatment (IDPT) is crucial for mental health but complicated by overlapping emotions and the need for large, labeled datasets.
Creating emotion-aware labeled datasets for IDPT is time-consuming and faces challenges like vocabulary size and data source variability.
Personalized mental health interventions require efficient methods for extracting linguistic properties and segmenting emotions from text.

Purpose of the Study:

To enhance trainable instances for IDPT applications by developing a semantic clustering mechanism.
To improve the accuracy and efficiency of Natural Language Processing (NLP) models in identifying mental health conditions, specifically depression symptoms.
To leverage unlabeled online forum data for training more robust personalized mental health interventions.

Main Methods:

Proposed a method combining Natural Language Processing (NLP) with attention-based in-depth entropy active learning.
Utilized synonym expansion via semantic vectors for clustering unlabeled text based on contextual semantic information.
Implemented a cyclical active learning process where selected unlabeled text is iteratively added to the training set to update the model.

Main Results:

The proposed synonym expansion semantic vectors enhanced training accuracy without negatively impacting performance.
A bidirectional Long Short-Term Memory (LSTM) network with an attention mechanism achieved a 0.85 Receiver Operating Characteristic (ROC) curve on a blind test set.
The method improved the detection rate of depression symptoms from online forum text by effectively utilizing unlabeled data.

Conclusions:

The developed semantic clustering and synonym expansion method is effective in increasing trainable instances for NLP in mental health.
Attention-based LSTM models demonstrate strong performance in classifying mental health symptoms using learned embeddings.
This approach offers a viable solution for improving personalized mental health interventions by efficiently processing large volumes of unlabeled online data.