Yingjun Yang
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A Multimodal large language model-based triage tool for osteoporotic vertebral compression fractures using posture and movement videos
Abstract Osteoporotic vertebral compression fracture (OVCF) is common yet frequently underdiagnosed due to nonspecific symptoms and limited access to definitive imaging. We developed a two-stage screening framework that integrates prompt-guided multimodal large language model (LLM) feature extraction with machine-learning classification. In a multicenter cohort of 204 participants (102 OVCF, 102 controls), standardized posture images and functional videos were processed using pose estimation.
Knowledge distillation for named entity recognition in traditional chinese medicine
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Abstract Named entity recognition (NER) in traditional Chinese medicine (TCM) text is central to the structuring and intelligent application of TCM knowledge. TCM case texts are characterized by sparse entity distributions and limited domain-specific data. Existing methods often struggle to recognize low-frequency entities and show limited capacity for effective knowledge transfer. This paper proposes a TCM NER framework incorporating structured knowledge distillation.
By Wangping Xiong, Hongda Huang, Yingjun Yang, Ming Yang Verified, Xiang Zhou, Shaobo Chen, Xin Cheng, Xian Zhou, Zhaoxing Xu
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Nature
Verified
Recurrent Male Hydrops Fetalis Reveals Hidden Incontinentia Pigmenti: Lessons From Pseudogene‐Interfered Genomic Diagnosis
Objective To report incontinentia pigmenti (IP) as an overlooked genetic etiology in three families with recurrent non-immune hydrops fetalis (NIHF), and to highlight the necessity of phenotype-driven targeted prenatal testing and multidisciplinary care in the next-generation sequencing (NGS) era. Methods Three unrelated families with recurrent male NIHF and negative chromosomal microarray analysis/NGS results were investigated.
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