# Transferability of Neural Network Clinical De-identification Systems

arXiv: https://arxiv.org/abs/2102.08517 · HTML: https://mlsanity.com/paper/2102.08517/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Generalizing Across Domains via Cross-Gradient Training](https://mlsanity.com/paper/1804.10745/) (2018)
- [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://mlsanity.com/paper/2003.12815/) (2020)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [Transfer Learning for Named-Entity Recognition with Neural Networks](https://mlsanity.com/paper/1705.06273/) (2017)
- [Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records](https://mlsanity.com/paper/2001.05714/) (2020)

## Built on by (most cited first, 5 of 5)

- [The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health](https://mlsanity.com/paper/2301.05571/) (2023)
- [A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models](https://mlsanity.com/paper/2403.18975/) (2024)
- [RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings](https://mlsanity.com/paper/2603.22820/) (2026)
- [The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models](https://mlsanity.com/paper/2206.14181/) (2022)
- [Automated Identification of Incidentalomas Requiring Follow-Up: A Multi-Anatomy Evaluation of LLM-Based and Supervised Approaches](https://mlsanity.com/paper/2512.05537/) (2025)

## Abstract (arXiv)

Objective: Neural network de-identification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world situations, however, researchers often have limited or no in-house training data. Existing systems and external data can help jump-start de-identification on in-house data; however, the most efficient way of utilizing existing systems and external data is unclear. This article investigates the transferability of a state-of-the-art neural clinical de-identification system, NeuroNER, across a variety of datasets, when it is modified architecturally for domain generalization and when it is trained strategically for domain transfer. Methods and Materials: We conducted a comparative study of the transferability of NeuroNER using four clinical note corpora with multiple note types from two institutions. We modified NeuroNER architecturally to integrate two types of domain generalization approaches. We evaluated each architecture using three training strategies. We measured: transferability from external sources; transferability across note types; the contribution of external source data when in-domain training data are available; and transferability across institutions. Results and Conclusions: Transferability from a single external source gave inconsistent results. Using additional external sources consistently yielded an F1-score of approximately 80%. Fine-tuning emerged as a dominant transfer strategy, with or without domain generalization. We also found that external sources were useful even in cases where in-domain training data were available. Transferability across institutions differed by note type and annotation label but resulted in improved performance.