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Yu Yin

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Articles

DSCrisk: A dual-view and substructure aware based method for drug-drug interaction risk-level prediction enhanced by contrastive learning

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By Qingwen Li, Yu Yin
| Taylor & Francis Online
Combination pharmacotherapy remains a fundamental strategy in contemporary clinical practice. However, the concurrent use of multiple drugs substantially increases the risk of harmful drug–drug interaction events (DDIEs). Developing reliable computational methods for DDIE detection is therefore essential, yet existing approaches still face notable limitations in modelling interaction-relevant chemistry, relational context, and interpretability.
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An explainable artificial intelligence framework using electrocardiograms to predict mortality and major adverse cardiovascular events

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By Jerry Peng, Michael D. Glidden, Sai Rahul Ponnana, Santosh K. Sirasapalli, Tong Zhang, Mark Yoder, Nour Tashtish, Judith A. Mackall, Ravi S. Ramani, Sadeer G Al-Kindi, Yu Yin, Jing Ma, Vipin Chaudhary, Sanjay Rajagopalan
| Nature Verified
Abstract Artificial intelligence (AI) algorithms can detect subtle patterns in electrocardiograms (ECGs) to predict cardiovascular risk. Though accurate, these models are often unable to explain the factors driving their predictions, limiting interpretability and clinical adoption.
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LEME: open large language models for ophthalmology with advanced reasoning and clinical validation

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By Hyunjae R. Kim, Xuguang Ai, Sahana Srinivasan, Aidan Gilson, Krithi Pushpanathan, Qianqian Xie, JungWoo Park, Serina S Applebaum, Gabriel Dawei Yang, Minjie Zou, David Chen, Ke Zou, Soshian Sarrafpour, Ji Liu, Yu Yin, Jimin Huang, Quang Nguyen, Erping Long, Peixing Wan, Dianbo Liu, Richard Hintz, W. Zheng, Sophia Y. Wang, Lucila Ohno-Machado, Hua Xu, Ron A. Adelman, Yih-Chung Tham, Qingyu Chen, Luciano V. Del Priore
| Nature Verified
Abstract Large language models (LLMs) offer potential to alleviate the growing public health burden of eye disease; however, few models have been tailored for ophthalmology or evaluated against clinically relevant benchmarks and real-world workflows.
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