# Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks

arXiv: https://arxiv.org/abs/1901.01592 · HTML: https://mlsanity.com/paper/1901.01592/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data](https://mlsanity.com/paper/2401.06774/) (2023)
- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference](https://mlsanity.com/paper/2312.14646/) (2023)
- [Why KDAC? A general activation function for knowledge discovery](https://mlsanity.com/paper/2111.13858/) (2021)
- [Guided Adversarial Robust Transfer Learning with Source Mixing](https://mlsanity.com/paper/2309.06534/) (2023)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Named Entity Recognition in Unstructured Medical Text Documents](https://mlsanity.com/paper/2110.15732/) (2021)
- [Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity](https://mlsanity.com/paper/2512.17769/) (2025)
- [HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models](https://mlsanity.com/paper/2503.04930/) (2025)

## Abstract (arXiv)

Neural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2,3], could be said in natural language processing (NLP) tasks, such as named entity recognition. However, the success of NNs remains dependent on the availability of large labelled datasets, which is a significant hurdle in many important applications. One such case are electronic health records (EHRs), which are arguably the largest source of medical data, most of which lies hidden in natural text [4,5]. Data access is difficult due to data privacy concerns, and therefore annotated datasets are scarce. With scarce data, NNs will likely not be able to extract this hidden information with practical accuracy. In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4.3 above the architecture that won the competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms. To reach this state-of-the-art accuracy, our approach applies transfer learning to leverage on datasets annotated for other I2B2 tasks, and designs and trains embeddings that specially benefit from such transfer.