# NOWJ @BioCreative IX ToxHabits: An Ensemble Deep Learning Approach for Detecting Substance Use and Contextual Information in Clinical Texts

arXiv: https://arxiv.org/abs/2602.09469 · HTML: https://mlsanity.com/paper/2602.09469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)
- [The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health](https://mlsanity.com/paper/2301.05571/) (2023)

## Abstract (arXiv)

Extracting drug use information from unstructured Electronic Health Records remains a major challenge in clinical Natural Language Processing. While Large Language Models demonstrate advancements, their use in clinical NLP is limited by concerns over trust, control, and efficiency. To address this, we present NOWJ submission to the ToxHabits Shared Task at BioCreative IX. This task targets the detection of toxic substance use and contextual attributes in Spanish clinical texts, a domain-specific, low-resource setting. We propose a multi-output ensemble system tackling both Subtask 1 - ToxNER and Subtask 2 - ToxUse. Our system integrates BETO with a CRF layer for sequence labeling, employs diverse training strategies, and uses sentence filtering to boost precision. Our top run achieved 0.94 F1 and 0.97 precision for Trigger Detection, and 0.91 F1 for Argument Detection.