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Your search keyword '"Wang, Liqin"' showing total 526 results
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526 results on '"Wang, Liqin"'

1. Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models

Author

Chen, Feng, Wang, Liqin, Hong, Julie, Jiang, Jiaqi, and Zhou, Li

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. Yet, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to detect and mitigate diverse forms of bias in AI models developed using EHR data. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 1, 2010, and Dec 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development process, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing six major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks in healthcare settings. Four studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Sixty proposed various strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance (e.g., accuracy, AUROC) and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling, reweighting, and transformation. Discussion: This review highlights the varied and evolving nature of strategies to address bias in EHR-based AI models, emphasizing the urgent needs for the establishment of standardized, generalizable, and interpretable methodologies to foster the creation of ethical AI systems that promote fairness and equity in healthcare., Comment: Published in JAMIA Volume 31, Issue 5, May 2024

Published
2023

2. Question Feature Enhanced Knowledge Tracing Model

Author

XU Zhihong, ZHANG Huibin, DONG Yongfeng, WANG Liqin, WANG Xu

Subjects
knowledge tracing, feature enhancement, graph convolutional neural network, contrastive learning, attention mechanism, Electronic computers. Computer science, QA75.5-76.95
Abstract

Knowledge tracing involves tracking students’ knowledge state in real-time based on their past answering performance and predicting their future performance, which is crucial for personalized education. In recent years, RNN-based deep knowledge tracing models have gradually become the mainstream research approach in the field of knowledge tracing. However, existing knowledge tracing models suffer from the inability to capture long-term dependencies between sequences and the neglect of the relationship between questions and knowledge points, resulting in insufficient extraction of question features. To address these issues, a knowledge tracing model based on question feature enhancement (QFEKT) is proposed. Firstly, graph convolutional neural network is used to model the relevant features of questions and knowledge points, and contrastive learning is introduced to enhance feature representation. Then, the question features are further enhanced through question matching module and student knowledge state representation module. The question matching module extracts similar questions as a complement to the question features. The student knowledge state representation module combines bidirectional long short-term memory networks with attention mechanism to enhance question features and model students’ knowledge state. Finally, the prediction module integrates similar question features and student knowledge state to predict students’ future performance. Comparative experiments on three publicly available real-world datasets demonstrate that the QFEKT model outperforms other baseline models in knowledge tracing tasks and exhibits significant advantages in predicting students’ future performance.

Published
2024
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3. Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation

Author

Wang, Yifei, Zhou, Zhengyang, Wang, Liqin, Laurentiev, John, Hou, Peter, Zhou, Li, and Hong, Pengyu

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, J.3
Abstract

When using machine learning (ML) to aid decision-making, it is critical to ensure that an algorithmic decision is fair and does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing group fairness methods aim to ensure equal outcomes (such as loan approval rates) across groups delineated by protected variables like race or gender. However, these methods overlook the intricate, inherent differences among these groups that could influence outcomes. The confounding factors, which are non-protected variables but manifest systematic differences, can significantly affect fairness evaluation. Therefore, we recommend a more refined and comprehensive approach that accounts for both the systematic differences within groups and the multifaceted, intertwined confounding effects. We proposed a fairness metric based on counterparts (i.e., individuals who are similar with respect to the task of interest) from different groups, whose group identities cannot be distinguished algorithmically by exploring confounding factors. We developed a propensity-score-based method for identifying counterparts, avoiding the issue of comparing "oranges" with "apples". In addition, we introduced a counterpart-based statistical fairness index, called Counterpart-Fairness (CFair), to assess the fairness of ML models. Various empirical studies were conducted to validate the effectiveness of CFair., Comment: 24 pages, 9 figures, 14 tables

Published
2023

4. Research on Knowledge Graph Entity Prediction Method of Multi-modal Curriculum Learning

Author

XU Zhihong, HAO Xuemei, WANG Liqin, DONG Yongfeng, WANG Xu

Subjects
curriculum learning, multi-modal, generative adversarial network (gan), negative sample, Electronic computers. Computer science, QA75.5-76.95
Abstract

On the one hand, the existing knowledge graph entity prediction methods only use the neighborhood and graph structure information to enhance the node information, and ignore the multi-modal information outside the knowledge graph to enhance the knowledge graph information. On the other hand, when comparing positive and negative samples to train the model, the negative sample random ordering results in poor training effect, and there is no additional information to help the training process of negative samples. Therefore, a multi-modal curriculum learning knowledge graph entity prediction model (MMCL) is proposed. Firstly, multi-modal information is introduced into the knowledge graph to achieve information enhancement, and the multi-modal information fusion process is optimized using generative adversarial network (GAN). The samples generated by the generator enhance the knowledge graph information, and at the same time improve the discriminator??s ability to distinguish the truth and falsity of triples. Secondly, the course learning algorithm is used to sort the negative samples from easy to difficult according to the difficulty of the negative samples. By adding the sorted negative samples into the training process hierarchically through the pace function, it is more beneficial to playing the effect of negative samples in identifying the truth and falseness of triples, and at the same time, no label learning avoids the false-negative problem in the late training period. The discriminators share parameters with course learning training models to help improve the training effect of negative samples. Experiments are conducted on two datasets, FB15k-237 and WN18RR. The results show that compared with the baseline model, MMCL is significantly improved in mean reciprocal rank (MRR), Hits@1, Hits@3 and Hits@10. The validity and feasibility of the proposed model are verified.

Published
2024
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8. Research on Accelerated Degradation Test and Reliability Evaluation Method for Hydraulic Pumps Based on Parallel Energy Saving

Author

Qi, Jianjun, Chen, Shaojia, Wang, Dongfeng, Wang, Liqin, Guo, Rui, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Halgamuge, Saman K., editor, Zhang, Hao, editor, Zhao, Dingxuan, editor, and Bian, Yongming, editor

Published
2024
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22. QDPR deficiency drives immune suppression in pancreatic cancer

Author

Liu, Ji, He, Xiaowei, Deng, Shuang, Zhao, Sihan, Zhang, Shaoping, Chen, Ziming, Xue, Chunling, Zeng, Lingxing, Zhao, Hongzhe, Zhou, Yifan, Bai, Ruihong, Xu, Zilan, Liu, Shaoqiu, Zhou, Quanbo, Li, Mei, Zhang, Jialiang, Huang, Xudong, Chen, Rufu, Wang, Liqin, Lin, Dongxin, and Zheng, Jian

Published
2024
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28. Targeting of vulnerabilities of drug-tolerant persisters identified through functional genetics delays tumor relapse

Author

Chen, Mengnuo, Mainardi, Sara, Lieftink, Cor, Velds, Arno, de Rink, Iris, Yang, Chen, Kuiken, Hendrik J., Morris, Ben, Edwards, Finn, Jochems, Fleur, van Tellingen, Olaf, Boeije, Manon, Proost, Natalie, Jansen, Robin A., Qin, Shifan, Jin, Haojie, Koen van der Mijn, J.C., Schepers, Arnout, Venkatesan, Subramanian, Qin, Wenxin, Beijersbergen, Roderick L., Wang, Liqin, and Bernards, René

Published
2024
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42. cFLIP suppression and DR5 activation sensitize senescent cancer cells to senolysis

Author

Wang, Liqin, Jin, Haojie, Jochems, Fleur, Wang, Siying, Lieftink, Cor, Martinez, Isabel Mora, De Conti, Giulia, Edwards, Finn, de Oliveira, Rodrigo Leite, Schepers, Arnout, Zhou, Yangyang, Zheng, Jiaojiao, Wu, Wei, Zheng, Xingling, Yuan, Shengxian, Ling, Jing, Jastrzebski, Kathy, Santos Dias, Matheus Dos, Song, Ji-Ying, Celie, Patrick N. H., Yagita, Hideo, Yao, Ming, Zhou, Weiping, Beijersbergen, Roderick L., Qin, Wenxin, and Bernards, René

Published
2022
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