# A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024

arXiv: https://arxiv.org/abs/2410.17963 · HTML: https://mlsanity.com/paper/2410.17963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)
- [A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams](https://mlsanity.com/paper/1905.08772/) (2019)
- [Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023](https://mlsanity.com/paper/2310.20003/) (2023)
- [Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023](https://mlsanity.com/paper/2310.19970/) (2023)

## Built on by (most cited first, 1 of 1)

- [Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025](https://mlsanity.com/paper/2511.23325/) (2025)

## Abstract (arXiv)

The eRisk laboratory aims to address issues related to early risk detection on the Web. In this year's edition, three tasks were proposed, where Task 2 was about early detection of signs of anorexia. Early risk detection is a problem where precision and speed are two crucial objectives. Our research group solved Task 2 by defining a CPI+DMC approach, addressing both objectives independently, and a time-aware approach, where precision and speed are considered a combined single-objective. We implemented the last approach by explicitly integrating time during the learning process, considering the ERDEθ metric as the training objective. It also allowed us to incorporate temporal metrics to validate and select the optimal models. We achieved outstanding results for the ERDE50 metric and ranking-based metrics, demonstrating consistency in solving ERD problems.