# Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced GPU Demands: A Case Study in Dementia Identification

arXiv: https://arxiv.org/abs/2504.12494 · HTML: https://mlsanity.com/paper/2504.12494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://mlsanity.com/paper/2206.01861/) (2022)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [Structured Pruning of Large Language Models](https://mlsanity.com/paper/1910.04732/) (2019)
- [Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python](https://mlsanity.com/paper/2106.07799/) (2021)
- [A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks](https://mlsanity.com/paper/1909.13568/) (2019)
- [FastContext: an efficient and scalable implementation of the ConText algorithm](https://mlsanity.com/paper/1905.00079/) (2019)

## Abstract (arXiv)

Clinical natural language processing (NLP) is increasingly in demand in both clinical research and operational practice. However, most of the state-of-the-art solutions are transformers-based and require high computational resources, limiting their accessibility. We propose a hybrid NLP framework that integrates rule-based filtering, a Support Vector Machine (SVM) classifier, and a BERT-based model to improve efficiency while maintaining accuracy. We applied this framework in a dementia identification case study involving 4.9 million veterans with incident hypertension, analyzing 2.1 billion clinical notes. At the patient level, our method achieved a precision of 0.90, a recall of 0.84, and an F1-score of 0.87. Additionally, this NLP approach identified over three times as many dementia cases as structured data methods. All processing was completed in approximately two weeks using a single machine with dual A40 GPUs. This study demonstrates the feasibility of hybrid NLP solutions for large-scale clinical text analysis, making state-of-the-art methods more accessible to healthcare organizations with limited computational resources.