Neural natural language processing for unstructured data in electronic health records: a review

I Li, J Pan, J Goldwasser, N Verma, WP Wong… - Computer Science …, 2022 - Elsevier
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing

S Sousa, R Kern - Artificial Intelligence Review, 2023 - Springer
Deep learning (DL) models for natural language processing (NLP) tasks often handle
private data, demanding protection against breaches and disclosures. Data protection laws …

Large language models can be strong differentially private learners

X Li, F Tramer, P Liang, T Hashimoto - arxiv preprint arxiv:2110.05679, 2021 - arxiv.org
Differentially Private (DP) learning has seen limited success for building large deep learning
models of text, and straightforward attempts at applying Differentially Private Stochastic …

[หนังสือ][B] Synthetic data for deep learning

SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Selective differential privacy for language modeling

W Shi, A Cui, E Li, R Jia, Z Yu - arxiv preprint arxiv:2108.12944, 2021 - arxiv.org
With the increasing applications of language models, it has become crucial to protect these
models from leaking private information. Previous work has attempted to tackle this …

Knowledge graph and deep learning-based text-to-GraphQL model for intelligent medical consultation chatbot

P Ni, R Okhrati, S Guan, V Chang - Information Systems Frontiers, 2024 - Springer
Abstract Text-to-GraphQL (Text2GraphQL) is a task that converts the user's questions into
Graph+ QL (Query Language) when a graph database is given. That is a task of semantic …

Exploring transformer text generation for medical dataset augmentation

A Amin-Nejad, J Ive, S Velupillai - Proceedings of the Twelfth …, 2020 - aclanthology.org
Abstract Natural Language Processing (NLP) can help unlock the vast troves of unstructured
data in clinical text and thus improve healthcare research. However, a big barrier to …

Publicly shareable clinical large language model built on synthetic clinical notes

S Kweon, J Kim, J Kim, S Im, E Cho, S Bae, J Oh… - arxiv preprint arxiv …, 2023 - arxiv.org
The development of large language models tailored for handling patients' clinical notes is
often hindered by the limited accessibility and usability of these notes due to strict privacy …

Lightweight transformers for clinical natural language processing

O Rohanian, M Nouriborji, H Jauncey… - Natural language …, 2024 - cambridge.org
Specialised pre-trained language models are becoming more frequent in Natural language
Processing (NLP) since they can potentially outperform models trained on generic texts …

[HTML][HTML] Generating synthetic training data for supervised de-identification of electronic health records

CA Libbi, J Trienes, D Trieschnigg, C Seifert - Future Internet, 2021 - mdpi.com
A major hurdle in the development of natural language processing (NLP) methods for
Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns …