Biomedical Named Entity Recognition via A Hybrid Neural Network Model

Wei, Hao; Gao, Mingyuan; Zhou, Ai; Chen, Fei; Wen, Quan; Wang, Chunli; Lu, Mingyu

doi:10.1109/iske47853.2019.9170320

Cited by 3 publications

(1 citation statement)

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“…Ref. [ 15 ] presents public general pre-trained word embeddings as input features and employs a hybrid dilation convolution structure and the attention mechanism to improve the two networks. The model achieved a reasonable F1 score of 73.72% on the JNLPBA corpus and is the first to adopt the hybrid structure that combines CNN with BLSTM in BNER.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

2023

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The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.

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Section: Related Workmentioning

confidence: 99%