Changchang Yin

Short Biography [CV]

I’m a Ph.D. student at the Department of Computer Science and Engineering, The Ohio State University (OSU). I’m fortunately working with Prof. Ping Zhang in AIMed (Artificial Intelligence in Medicine) Lab. My research interests lie in data mining, machine learning and their application to trustworthy AI (e.g., fairness and causal inference), computational medicine (e.g., predictive modeling, patient subtyping and medical imaging).

Publications

• SepsisLab: Early sepsis prediction with uncertainty quantification and active sensing
  Changchang Yin, Pin-Yu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey Caterino, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024
  [Paper]

• Predictive modeling with temporal graphical representation on electronic health records
  Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang
  International Joint Conference on Artificial Intelligence (IJCAI), 2024 (Main track, oral presentation)
  [Paper]

• Rethinking human-AI collaboration in complex medical decision making: A case study in sepsis diagnosis
  Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M Padilla, Jeffrey Caterino, Ping Zhang, Dakuo Wang
  ACM CHI Conference on Human Factors in Computing Systems (CHI), 2024 (Acceptance rate: 1060/4028 = 26.3%)
  [Paper]

• Multimodal risk prediction with physiological signals, medical images and clinical notes
  Yuanlong Wang, Changchang Yin, Ping Zhang
  Heliyon, 10:e26772, 2024
  [Paper]

• Stable clinical risk prediction against distribution shift in electronic health records
  Seungyeon Lee, Changchang Yin, Ping Zhang
  Patterns 4:100828, 2023 (Impact factor: 6.5)
  [Paper]

• A fair and interpretable network for clinical risk prediction: A regularized multi-view multi-task learning approach
  Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
  Knowledge and Information Systems (KAIS) 65:1487–1521, 2023
  [Paper]

• Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes
  Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD 2022. (Acceptance rate: 254/1695 = 15.0%, research track)
  [Paper] [Code]

• Predicting age-related macular degeneration progression with contrastive attention and time-aware LSTM
  Changchang Yin, Sayoko Moroi, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD 2022. (Acceptance rate: 197/753 = 25.9%, applied science track)
  [Paper] [Code]

• Cardiac complication risk profiling for cancer survivors via multi-view multi-task learning
  Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
  IEEE International Conference on Data Mining, ICDM 2021 (Acceptance rate: 98/990 = 9.9%, regular paper, oral presentation. Selected as one of the best papers and invited to KAIS Journal Special Issue)
  [Paper] [Code]

• Temporal clustering with external memory network for disease progression modeling
  Zicong Zhang, Changchang Yin, Ping Zhang
  IEEE International Conference on Data Mining, ICDM 2021 (Acceptance rate: 98/990 = 9.9%, regular paper, oral presentation)
  [Paper] [Code]

• Contrastive attention for automatic medical report generation
  Fenglin Liu, Changchang Yin, Xian Wu, Shen Ge, Ping Zhang, Xu Su
  Findings of Annual Meeting of the Association for Computational Linguistics (Findings of ACL), 2021
  [Paper]

• A survival model generalized to regression learning algorithms
  Yuanfang Guan, Hongyang Li, Daiyao Yi, Dongdong Zhang, Changchang Yin, Keyu Li, Ping Zhang.
  Nature Computational Science 1:433–440, 2021
  [Paper]

• Brain atlas guided attention U-net for white matter hyperintensity segmentation
  Zicong Zhang, Kimerly Powell, Changchang Yin, Shilei Cao, Dani Gonzalez, Yousef Hannawi, Ping Zhang
  American Medical Informatics Association Informatics Summit, AMIA Summit 2021 (Buckeye AI, one of the top solutions for WMH Segmentation Challenge)
  [Paper] [Code]

• An interpretable deep-learning model for early prediction of sepsis in the emergency department
  Dongdong Zhang, Changchang Yin, Katherine Hunold, Xiaoqian Jiang, Jeffrey Caterino, Ping Zhang
  Patterns 2:100196, 2021 (Buckeye AI, one of the winning teams for 2019 DII National Data Science Challenge)
  [Paper] [Code]

• An interpretable risk prediction model for healthcare with pattern attention
  Sundreen Asad Kamal, Changchang Yin, Buyue Qian, Ping Zhang
  BMC Medical Informatics and Decision Making 20:307, 2020
  [Paper] [Code]

• Combining structured and unstructured data for predictive models: a deep learning approach
  Dongdong Zhang, Changchang Yin, Jucheng Zeng, Xiaohui Yuan, Ping Zhang
  BMC Medical Informatics and Decision Making 20:280, 2020
  [Paper] [Code]

• Marrying medical domain knowledge with deep learning on electronic health records: a deep visual analytics approach
  Rui Li, Changchang Yin, Samuel Yang, Buyue Qian, Ping Zhang
  Journal of Medical Internet Research (JMIR) 22(9):e20645, 2020 (Impact factor: 5.034. Featured on AMIA 2021 Year-in-Review)
  [Paper] [Code]

• Study on automatic detection and classification of breast nodule using deep convolutional neural network system
  Feiqian Wang, Xiaotong Liu, Na Yuan, Buyue Qian, Litao Ruan, Changchang Yin, Ciping Jin
  Journal of Thoracic Disease
  [Paper]

• Estimating Individual Treatment Effects with Time-Varying Confounders
  Ruoqi Liu, Changchang Yin, Ping Zhang
  IEEE International Conference on Data Mining. ICDM 2020. (Acceptance rate: 91/930 = 9.8%, regular paper, oral presentation)
  [Paper] [Code]

• Identifying sepsis subphenotypes via time-aware multi-modal auto-encoder
  Changchang Yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD 2020. (Acceptance rate: 216/1279 = 16.9%, research track)
  [Paper] [Code] [Video]

• Domain knowledge guided deep learning with electronic health records
  Changchang Yin, Rongjian Zhao, Buyue Qian, Xin Lv, Ping Zhang.
  IEEE International Conference on Data Mining, ICDM 2019 (Acceptance rate: 95/1046 = 9.1%, regular paper, oral presentation)
  [Paper] [Code]

• Automatic generation of medical imaging diagnostic report with hierarchical recurrent neural network.
  Changchang Yin, Buyue Qian, Xianli Zhang, Yang Li, Qinghua Zheng.
  IEEE International Conference on Data Mining, ICDM 2019 (Acceptance rate: 95/1046 = 9.1%, regular paper, oral presentation)
  [Paper]

• KnowRisk: an interpretable knowledge-guided model for disease risk prediction
  Xianli Zhang, Buyue Qian, Yang Li, Changchang Yin, Xudong Wang, Qinghua Zheng
  IEEE International Conference on Data Mining, ICDM 2019 (Acceptance rate: 95/1046 = 9.1%, regular paper, oral presentation)
  [Paper]

• Classifying breast cancer histopathological images using a robust artificial neural network architecture
  Xianli Zhang, Yinbin Zhang, Buyue Qian, Xiaotong Liu, Xiaoyu Li, Xudong Wang, Changchang Yin, Xin Lv, Lingyun Song, Liang Wang
  International Work-Conference on Bioinformatics and Biomedical Engineering
  [Paper]

• Spatial attention lesion detection on automated breast ultrasound
  Feiqian Wang, Xiaotong Liu, Buyue Qian, Litao Ruan, Rongjian Zhao, Changchang Yin, Na Yuan, Rong Wei, Xin Ma, Jishang Wei
  International Work-Conference on Bioinformatics and Biomedical Engineering
  [Paper]

• Deep similarity-based batch mode active learning with exploration-exploitation.
  Changchang Yin, Buyue Qian, Shilei Cao, Xiaoyu Li, Jishang Wei, Qinghua Zheng, Ian Davidson
  IEEE International Conference on Data Mining, ICDM 2017 (Regular paper, oral presentation)
  [Paper]

• Knowledge guided short-text classification for healthcare applications
  Shilei Cao, Buyue Qian, Changchang Yin, Xiaoyu Li, Jishang Wei, Qinghua Zheng, Ian Davidson
  IEEE International Conference on Data Mining, ICDM 2017 (Regular paper, oral presentation)
  [Paper]

• Measuring patient similarities via a deep architecture with medical concept embedding.
  Zihao Zhu, Changchang Yin, Buyue Qian, Yu Cheng, Jishang Wei, Fei Wang
  IEEE International Conference on Data Mining, ICDM 2016 (Regular paper, oral presentation)
  [Paper]

Services

• Conference reviewer: KDD, AMIA
• Journal reviewer:JMIR, JBHI, NCAA, BMC Medical Informatics and Decision Making
• Conference sub reviewer: ICML, NeurIPS, AAAI, IJCAI, ICML, ICDM, SDM
• Journal sub reviewer: Nature Communications, Nature Machine Intelligence, Patterns, BMC Bioinformatics

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