Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

Journals

1. Zhang T, Lin H, Ren Y, Yang Z, Wang J, Zhang S, Xu B, Duan X. Adversarial transfer network with bilinear attention for the detection of adverse drug reactions from social media. Applied Soft Computing 2021;106:107358 View
2. Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Medical Informatics 2021;9(10):e23898 View
3. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
4. Liang Z, Zhang Z, Chen H, Zhang Z. Disease prediction based on multi-type data fusion from Chinese electronic health record. Mathematical Biosciences and Engineering 2022;19(12):13732 View
5. Gu Z, He X, Yu P, Jia W, Yang X, Peng G, Hu P, Chen S, Chen H, Lin Y. Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model. Artificial Intelligence in Medicine 2024;150:102822 View
6. Wang L, Ma Y, Bi W, Lv H, Li Y. An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study. Journal of Medical Internet Research 2024;26:e54580 View
7. Akagi A, Narimoto K, Ueda K, Koizumi N, Okumura N, Tanito M. Clinical parameters associated with blood pressure in patients with primary open-angle glaucoma and exfoliation glaucoma. Graefe's Archive for Clinical and Experimental Ophthalmology 2025 View
8. Gou X, Yao J, Lai W, Gao Y, Wang S, Zhou C, Ye H, Tian J, Yi J, Cao D. A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations. BMC Medical Informatics and Decision Making 2025;25(1) View