# Neural language models for text classification in evidence-based medicine

arXiv: https://arxiv.org/abs/2012.00584 · HTML: https://mlsanity.com/paper/2012.00584/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Zero-shot Generative Large Language Models for Systematic Review Screening Automation](https://mlsanity.com/paper/2401.06320/) (2024)
- [User Perception of Attention Visualizations: Effects on Interpretability Across Evidence-Based Medical Documents](https://mlsanity.com/paper/2508.10004/) (2025)
- [Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups](https://mlsanity.com/paper/2405.13011/) (2024)

## Abstract (arXiv)

The COVID-19 has brought about a significant challenge to the whole of humanity, but with a special burden upon the medical community. Clinicians must keep updated continuously about symptoms, diagnoses, and effectiveness of emergent treatments under a never-ending flood of scientific literature. In this context, the role of evidence-based medicine (EBM) for curating the most substantial evidence to support public health and clinical practice turns essential but is being challenged as never before due to the high volume of research articles published and pre-prints posted daily. Artificial Intelligence can have a crucial role in this situation. In this article, we report the results of an applied research project to classify scientific articles to support Epistemonikos, one of the most active foundations worldwide conducting EBM. We test several methods, and the best one, based on the XLNet neural language model, improves the current approach by 93% on average F1-score, saving valuable time from physicians who volunteer to curate COVID-19 research articles manually.