Image Data Analysis

• Yuan, H.☯︎, Hong, C.☯︎, … & Liu, N. (2024). Clinical Domain Knowledge-derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification. Journal of Biomedical Informatics. (Paper, Code)
• Yuan, H.☯︎, Hong, C.☯︎, … & Liu, N. (2024). Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation. Health Care Science. (Paper, Code)
• Yuan, H. & Hong, C. (2024). Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning. arXiv. (Paper, Code)
• Yuan, H., Jiang, P. & Zhao, G. (2023). Human-Guided Design to Explain Deep Learning-based Pneumothorax Classifier. Medical Imaging with Deep Learning, Short Paper Track. (Paper, Code)

Tabular Data Analysis

• Liu, P., Yuan, H., … & Peres, M. (2024). A Modified Gower Distance-based Clustering Analysis for Mixed-type Data. arXiv. (Paper)
• Yuan, H., … & Xie, F. (2023). Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making. International Conference on Learning Representations, Tiny Paper Track. (Paper, Code)
• Li, S., … Yuan, H., … & Liu, N. (2023). FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics. (Paper, Code)
• Xie, F., … Yuan, H., … & Liu, N. (2023). A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols. (Paper, Code)
• Yuan, H., … & Liu, N. (2022). AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics. (Paper, Code)
• Xie, F., Ning, Y., Yuan, H., … & Chakraborty, B. (2022). AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics. (Paper, Code)
• Miao, C., … Yuan, H., … & Wang, Z. (2021). TRIM37 orchestrates renal cell carcinoma progression via histone H2A ubiquitination-dependent manner. Journal of Experimental & Clinical Cancer Research. (Paper)
• Xie, F., Ning Y., Yuan, H., … & Liu, N. (2021). Package ‘AutoScore’: An Interpretable Machine Learning-Based Automatic Clinical Score Generator. R Package. (Paper, Code)
• Miao, C.☯︎, Yu, A.☯︎, Yuan, H.☯︎, … & Wang, Z. (2020). Effect of Enhanced Recovery After Surgery on Postoperative Recovery and Quality of Life in Patients Undergoing Laparoscopic Partial Nephrectomy. Frontiers in Oncology. (Paper)
• Zhang, J., Sun, Z., Yuan, H. & Wang, M. (2020). Alternatives to the Kaplan-Meier estimator of progression-free survival. The International Journal of Biostatistics. (Paper)

Text Data Analysis

• Zhao, Y.☯︎, Yuan, H.☯︎ & Wu, Y. (2021). Prediction of Adverse Drug Reaction using Machine Learning Based on an Imbalanced Electronic Medical Records Dataset. International Conference on Medical and Health Informatics, Full Paper Track. (Paper)

Multi-modality Analysis

• Li, Y., … Yuan, H., … & Ang, M. (2024). Long-term effect of Childhood Atropine Treatment on Choroidal Thickness using Deep Learning enabled Segmentation – The Atropine Treatment Long-term Assessment Study. arXiv. (Paper)

General Machine Learning

• Kang, L., … Yuan, H. & Zhu, C. (2024). Approximate Policy Iteration with Deep Minimax Average Bellman Error Minimization. IEEE Transactions on Neural Networks and Learning Systems. (Paper)
• Yuan, H.☯︎, Liu, M.☯︎, … & Wu, Y. (2023). An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations for deep learning models. International Conference on Learning Representations, Tiny Paper Track. (Paper, Code)
• Kang, L., Yuan, H. & Zhu C. (2023). Error Analysis of Fitted Q-iteration with ReLU-activated Deep Neural Networks. International Conference on Learning Representations, Tiny Paper Track. (Paper)
• Liu, M., Ning Y., Yuan, H., … & Liu, N. (2022). Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making. arXiv. (Paper)

Review and Perspective

• Yuan, H.☯︎, Yu, K.☯︎, Xie, F.☯︎ & Liu, M. (2024). Automated Machine Learning with Interpretation: A Systematic Review of Methodologies and Applications in Healthcare. Medicine Advances. (Paper)
• Yuan, H., … & Fan, Z. (2024). Human-in-the-loop Machine Learning for Healthcare: Current Progress and Future Opportunities in Electronic Health Records. Medicine Advances. (Paper)
• Liu, M., Li, S., Yuan, H., … & Liu, N. (2023). Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine. (Paper)
• Xie, F.☯︎, Yuan, H.☯︎, … & Liu, N. (2021). Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics. (Paper)

☯︎ Equal Contribution