# Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity

arXiv: https://arxiv.org/abs/2512.17769 · HTML: https://mlsanity.com/paper/2512.17769/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning](https://mlsanity.com/paper/2003.09288/) (2020)
- [Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks](https://mlsanity.com/paper/1901.01592/) (2019)
- [BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition](https://mlsanity.com/paper/2303.09306/) (2023)
- [Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT Semantic Embeddings K-Means-Infused CRF Model](https://mlsanity.com/paper/2401.17206/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Lightweight Hybrid Transformer-CRF Architecture for Multi-Type Bangla Medical Entity Recognition](https://mlsanity.com/paper/2605.25463/) (2026)
- [Automated Multilabel Mpox Research Classification with Explainable Transformer Models](https://mlsanity.com/paper/2607.26700/) (2026)

## Abstract (arXiv)

Medical Entity Recognition (MedER) is an essential NLP task for extracting meaningful entities from the medical corpus. Nowadays, MedER-based research outcomes can remarkably contribute to the development of automated systems in the medical sector, ultimately enhancing patient care and outcomes. While extensive research has been conducted on MedER in English, low-resource languages like Bangla remain underexplored. Our work aims to bridge this gap. For Bangla medical entity recognition, this study first examined a number of transformer models, including BERT, DistilBERT, ELECTRA, and RoBERTa. We also propose a novel Multi-BERT Ensemble approach that outperformed all baseline models with the highest accuracy of 89.58%. Notably, it provides an 11.80% accuracy improvement over the single-layer BERT model, demonstrating its effectiveness for this task. A major challenge in MedER for low-resource languages is the lack of annotated datasets. To address this issue, we developed a high-quality dataset tailored for the Bangla MedER task. The dataset was used to evaluate the effectiveness of our model through multiple performance metrics, demonstrating its robustness and applicability. Our findings highlight the potential of Multi-BERT Ensemble models in improving MedER for Bangla and set the foundation for further advancements in low-resource medical NLP.