提出时间感知方法，提升厌食症早期检测的精度与速度。
A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024
- 将时间因素融入学习过程，以统一优化检测精度与速度。
- 在ERDE50和排名指标上表现优异，结果稳定可靠。
- 适合关注医疗风险早期预警与时间序列建模的研究者。
eRisk实验室致力于解决网络环境中早期风险检测问题。2024年提出了三项任务，其中第二项为厌食症早期迹象的检测。早期风险检测需兼顾精度与速度。本研究通过定义CPI+DMC方法，分别优化两项目标，并进一步提出时间感知方法，将精度与速度视为单一目标联合优化。该方法在训练过程中显式引入时间信息，以ERDEθ为优化目标，并结合时间相关指标验证与选择最优模型。实验结果显示，在ERDE50及基于排名的指标上均取得优异表现，证明了该方法在解决ERD问题上的有效性与一致性。
原文摘要 · Abstract (English)
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.
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