Hu Danqing

Hu Danqing, Ph.D., is a lecturer at the School of Artificial Intelligence (College of Future Technology), Nanjing University of Information Science & Technology, and a core member of the Jiangsu University Key Laboratory of Intelligent Medical Image Computing and the Smart Medical Research Institute. He graduated from Shandong University with an undergraduate degree in 2014, and graduated from Zhejiang University with a master’s degree and a doctoral degree in 2017 and 2022 respectively. Research directions include medical artificial intelligence, medical informatics, lung cancer full-process decision support, etc. Currently, he presides over the National Natural Science Youth Fund, and participates in the Beijing Natural Science Foundation Haidian Original Innovation Key Project, the “13th Five-Year Plan” National Key Research Special Precision Medicine Research Project, and the National Natural Science Foundation General Project. Published 17 papers in important medical information journal conferences such as IEEE JBHI, AIM, IJMI, IEEE EMBC, etc., and was the first author of 14 articles. He applied for 11 invention patents, 4 first inventor authorizations, and 4 authorized software copyrights. He won the CHIP2018 Best Paper Award from the Chinese Information Society of China. He has long served as a reviewer for important journals in the field IEEE JBHI, AIM, JMIR, CMPB, ESWA, IJMI, and JBI.

For more information, please visit my personal homepage: https://faculty.nuist.edu.cn/hudanqing/zh_CN/index.htm

Research directions:

Medical artificial intelligence, medical informatics, early screening of lung cancer, accurate diagnosis of lymph node metastasis, prediction of lung cancer prognosis, recommendation of treatment options based on evidence-based medical evidence, medical natural language processing, multidisciplinary consultation clinical decision support system development, etc.

Representative papers (one work or communication):

[1] Danqing Hu , Shaolei Li, Nan Wu, Xudong Lu. A Multi-Modal Heterogeneous Graph Forest to Predict Lymph Node Metastasis of Non-Small Cell Lung Cancer. IEEE Journal of Biomedical and Health Informatics, 2023, 27(3): 1216-24.

[2] Danqing Hu , Shaolei Li, Zhengxing Huang, Nan Wu, Xudong Lu. Predicting postoperative non-small cell lung cancer prognosis via long short-term relational regularization. Artificial Intelligence in Medicine, 2020, 107: 101921.

[3] Danqing Hu# , Bing Liu, Xiaofeng Zhu, Xudong Lu, Nan Wu. Zero-shot information extraction from radiological reports using ChatGPT. International Journal of Medical Informatics, 2024, 183: 105321.

[4] Danqing Hu , Huanyao Zhang, Shaolei Li, Huilong Duan, Nan Wu, Xudong Lu. An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients. BMC Medical Informatics and Decision Making, 2022, 22(1): 245.

[5] Danqing Hu , Wei Dong, Xudong Lu, Huilong Duan, Kunlun He, Zhengxing Huang. Evidential MACE prediction of acute coronary syndrome using electronic health records. BMC Medical Informatics and Decision Making, 2019, 19(2): 61.

[6] Danqing Hu, Shaolei Li, Huanyao Zhang, Nan Wu, Xudong Lu. Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer with Electronic Medical Records: Development and Validation Study. JMIR Med Inform, 2022, 10(4): e35475.

[7] Danqing Hu, Huanyao Zhang, Shaolei Li, Yuhong Wang, Nan Wu, Xudong Lu. Automatic Extraction of Lung Cancer Staging Information from Computed Tomography Reports: Deep Learning Approach. JMIR Med Inform, 2021, 9(7): e27955-e.

[8] Danqing Hu, Zhengxing Huang, Tak-Ming Chan, Wei Dong, Xudong Lu, Huilong Duan. Acute coronary syndrome risk prediction based on GRACE risk score; Stud Health Technol Inform, 2018, 245: 398.

[9] Danqing Hu#, Bing Liu, Xiang Li, Hui Chen, Rui Guo, Lechao Cheng, Xudong Lu, Nan Wu. A Hierarchy-driven Multi-label Network with Label Constraints for Post-operative Complication Prediction of Lung Cancer; proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, F, 2023. IEEE.

[10] Danqing Hu#, Bing Liu, Lechao Cheng, Rui Guo, Jin Wang, Xudong Lu, Nan Wu. A Deep Multi-Task Network to Learn Tumor Pathological Representations for Lymph Node Metastasis Prediction. Stud Health Technol Inform, 2024, 310: 906-10.

[11] Danqing Hu# , Bing Liu, Xiaofeng Zhu, Xudong Lu, Nan Wu. Predicting Lymph Node Metastasis of Lung Cancer: A Two-stage Multimodal Data Fusion Approach; proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Orlando, United States, 2024. IEEE.

[12] Danqing Hu#, Bing Liu, Xiaofeng Zhu, Xudong Lu, Nan Wu. KAMLN: A Knowledge-aware Multi-label Network for Lung Cancer Complication Prediction; proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Orlando, United States, 2024. IEEE.

[13]Hui Su, Bing Liu, Xiaofeng Zhu, Xudong Lu, Nan Wu, Danqing Hu#. TGMT: A Terminology-Guided Multi-Task Approach for Lung Cancer CT Report Generation; proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Orlando, United States, 2024. IEEE.

Translated from the original Chinese source.