A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024
This work addresses early risk detection of anorexia for online platforms, but it is incremental as it builds on existing eRisk tasks and metrics.
The paper tackled early detection of anorexia from web data by proposing a time-aware approach that integrates time into the learning process, using the ERDEθ metric as the objective, and achieved outstanding results for ERDE50 and ranking-based metrics.
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.