# Automated Screening for Distress: A Perspective for the Future

arXiv: https://arxiv.org/abs/1902.09944 · HTML: https://mlsanity.com/paper/1902.09944/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition](https://mlsanity.com/paper/2501.10408/) (2025)

## Abstract (arXiv)

Distress is a complex condition which affects a significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide and other forms of psychological morbidity. Compelling evidence supports screening for distress as a means of facilitating early intervention and subsequent improvements in psychological well-being and overall quality of life. Nevertheless, despite the existence of evidence based and easily administered screening tools, for example, the Distress Thermometer, routine screening for distress is yet to achieve widespread implementation. Efforts are intensifying to utilise innovative, cost effective methods now available through emerging technologies in the informatics and computational arenas.