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Articles by Jiang Jun
An Adversarial Example Generation Method Based on Backward Dynamic Slope Gating
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The NSL-KDD dataset used in this study is publicly available from the IEEE Dataport repository (DOI: 10.21227/8rpg-qt98). The source code for the proposed DyReLU-Atk framework is available from the corresponding author upon reasonable request. References 1, , and , “ImageNet Classification With Deep Convolutional Neural Networks,” Communications of the ACM 60, no. 6 (2017): 84–90.
A multi-site benchmarking framework for scalable extraction of geriatric care constructs from electronic health records
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Abstract Current Natural Language Processing (NLP) algorithms for detecting geriatric conditions are largely limited to domain-specific models that fail to capture the interdependent, multidimensional nature of comprehensive geriatric assessment.
By Sunyang Fu, Min Ji Kwak, Jaerong Ahn, Qiuhao Lu, Zhiyi Yue, Nan Wang, Huipeng Liu, Grace W. Giles, Alexa Cumming, Keziah M. Thomas, Jiang Jun, Ming Jie Huang, Xiaoyang Ruan, Andrew Wen, Liwei Wang, Erin Hommel, Yanshan Wang, Lichao Sun, Huiwen Xu, Jennifer St. Sauver, Jeffrey S. Wefel, Nahid Rianon, Dae Hyun Kim, Hongfang Liu, Shreyans Sanghvi
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Nature
Verified
A machine-assisted framework for systematic error analysis in clinical concept extraction
Abstract Error analysis is critical for evaluating and improving clinical natural language processing (NLP) models, yet no standardized framework exists to support systematic error analysis for clinical concept extraction. Here we present MedError, a machine-assisted, human-in-the-loop framework integrating a validated error taxonomy, large language model (LLM)-assisted classification, and a structured annotation workflow.
Art for the Countryside’s Sake
Jiang Jun is a curator. He has a doctorate in art theory and cultural studies.
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