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Explainable AI
Deep learning methods have been widely adopted in many areas and industries. But in certain applications, such as healthcare and finance, interpretation is critical for decision making and behavioral change. Our group focuses on the explainable AI methods on text, image, and tableau data with the aim to develop novel explainability methods to support decision making and enhance the possibility for behavioral changes. In particular,
In text, we use and develop the latest explainable AI methods on medical notes to understand the uncertainty during the diagnosis process by capturing the representation of hypothetical words in the medical notes to provide interpretations and understandings.
In image, we compare and identify bias in existing explainable AI methods in medical imaging diagnosis with the focus on positive and negative gradients, variety of pooling methods, and cross-validation to understand the contextualization of interpretation.
In tableau data, we develop novel explainable AI methods in health risk predication by combining ablation study with latest post-hoc explainable AI methods to generate interpretation statements with the cost related consequences to engage patients to make behavioral changes. Furthermore, reinforcement learning will be applied to generate the ecological feedback to improve the personalized care management with the enhancement of explainable AI.
研究兴趣
论文共 416 篇作者统计合作学者相似作者
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crossref(2024)
Information Processing & Managementno. 4 (2024): 103743
INFORMATION PROCESSING & MANAGEMENTno. 1 (2024): 103542-103542
INFORMATION PROCESSING & MANAGEMENTno. 1 (2024)
Chi-Yang Hsu, Kyle Cox, Jiawei Xu,Zhen Tan, Tianhua Zhai, Mengzhou Hu,Dexter Pratt,Tianlong Chen, Ziniu Hu,Ying Ding
arxiv(2024)
Jeremy J. Yang,Aaron Goff,David J. Wild,Ying Ding, Ayano Annis, Randy Kerber, Brian Foote,Anurag Passi, Joel L. Duerksen, Shelley London,Ana C. Puhl,Thomas R. Lane,
Tuberculosis (2024): 102500-102500
crossref(2024)
Information Processing & Managementno. 1 (2024): 103495-103495
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