Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction

TL;DR

This paper introduces SDCNL, a deep learning approach that uses unsupervised label correction to classify online Reddit content for distinguishing between depression and suicidal ideation, addressing label noise issues.

Contribution

It presents a novel unsupervised label correction method that does not require prior noise distribution, improving classification of depression and suicidal ideation from web data.

Findings

Strong performance across multiple models

Effective noise correction without prior noise info

Applicable to challenging online classification tasks

Abstract

Early detection of suicidal ideation in depressed individuals can allow for adequate medical attention and support, which in many cases is life-saving. Recent NLP research focuses on classifying, from a given piece of text, if an individual is suicidal or clinically healthy. However, there have been no major attempts to differentiate between depression and suicidal ideation, which is an important clinical challenge. Due to the scarce availability of EHR data, suicide notes, or other similar verified sources, web query data has emerged as a promising alternative. Online sources, such as Reddit, allow for anonymity that prompts honest disclosure of symptoms, making it a plausible source even in a clinical setting. However, these online datasets also result in lower performance, which can be attributed to the inherent noise in web-scraped labels, which necessitates a noise-removal process. Thus, we propose SDCNL, a suicide versus depression classification method through a deep learning approach. We utilize online content from Reddit to train our algorithm, and to verify and correct noisy labels, we propose a novel unsupervised label correction method which, unlike previous work, does not require prior noise distribution information. Our extensive experimentation with multiple deep word embedding models and classifiers display the strong performance of the method in anew, challenging classification application. We make our code and dataset available at https://github.com/ayaanzhaque/SDCNL