Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.

ABSTRACT:

Purpose

The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP).

Material and methods

The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate.

Results

The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Ad

SUBMITTER: Lee TF 

PROVIDER: S-EPMC10775485 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature