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portfolio
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publications
Alternatives to the Kaplan-Meier estimator of progression-free survival
Published in The International Journal of Biostatistics, 2020
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Effect of enhanced recovery after surgery on postoperative recovery and quality of life in patients undergoing laparoscopic partial nephrectomy
Published in Frontiers in Oncology, 2020
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TRIM37 orchestrates renal cell carcinoma progression via histone H2A ubiquitination-dependent manner
Published in Journal of Experimental & Clinical Cancer Research, 2021
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Prediction of adverse drug reaction using machine learning and deep learning based on an imbalanced electronic medical records dataset
Published in Proceedings of the International Conference on Medical and Health Informatics (Full Paper Track), 2021
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AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Published in Journal of Biomedical Informatics, 2021
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Package AutoScore
Published in R Package, 2021
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Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Published in Journal of Biomedical Informatics, 2021
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AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
Published in Journal of Biomedical Informatics, 2022
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A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes
Published in STAR Protocols, 2023
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Error Analysis of Fitted Q-iteration with ReLU-activated Deep Neural Networks
Published in Proceedings of the International Conference on Learning Representations (Tiny Paper Track), 2023
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An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
Published in Proceedings of the International Conference on Learning Representations (Tiny Paper Track), 2023
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Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making
Published in Proceedings of the International Conference on Learning Representations (Tiny Paper Track), 2023
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Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Published in Artificial Intelligence in Medicine, 2023
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Human-Guided Design to Explain Deep Learning-based Pneumothorax Classifier
Published in Proceedings of the Medical Imaging with Deep Learning (Short Paper Track), 2023
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FedScore: A Privacy-Preserving Framework for Federated Scoring System Development
Published in Journal of Biomedical Informatics, 2023
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talks
Prediction of adverse drug reaction using machine learning and deep learning based on an imbalanced electronic medical records dataset
Published:
Early prediction of adverse drug reaction (ADR) is crucial in clinical research. The development of electronic medical record (EMR) provides an excellent resource for retrospective studies to extract samples and establish models that can be used for prediction of clinical deterioration. However, classical statistical models like multivariate logistic regression (LR) may result in unreliable predictions when handling unbalanced datasets. To develop a trustworthy model on unbalanced ADR data, we first transformed the EMR including medical notes into numeric variables. Then we introduced support vector machine (SVM), random forest (RF), AdaBoost, XGBoost, and artificial neural network (ANN) to deal with the challenge of high dimensionality. Furthermore, we utilized the ensembling approach to tackle data imbalance. Finally, we analyzed potential model mechanisms to provide interpretability and compared methods from the perspective of procedure elapsed time. The results showed ensembling contributed considerable improvement in prediction ability of various machine intelligence models. Compared with the baseline, RF, AdaBoost and XGBoost presented superiority, and ANN without fine-tuning showed similar competence. The results of this study demonstrated the great potential of machine learning models in medical domain.
teaching
Teaching Volunteer at Yongji Primary School
Primary school course, Yongji Primary School, Tianjin, 2015