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

TL;DR

This paper presents a novel time-aware machine learning approach for early detection of anorexia signs online, optimizing both precision and speed by integrating temporal information into the learning process.

Contribution

It introduces a time-aware approach that explicitly incorporates temporal data into model training for early risk detection, improving performance on ERDE metrics.

Findings

Achieved outstanding results on ERDE50 metric

Demonstrated consistency in early anorexia detection

Validated the approach with ranking-based metrics

Abstract

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 ERDE{\theta} 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.