PhenoRareAI: Phenotype-based intelligent diagnosis for rare neuromuscular disorders of glycogen storage disease and spinal muscular atrophy [0.03%]
基于表型的罕见糖原贮积病和脊髓性肌萎缩症智能诊断系统 PhenoRareAI
Weiqi Zhai,Kexin Jiao,Ningning Wang et al.
Weiqi Zhai et al.
Rare neuromuscular diseases pose significant diagnostic challenges due to their genetic complexity and varied clinical presentations. Current diagnostic methods are often costly and inefficient, with primary care physician slacking adequate...
PKFAR: psychiatry knowledge-fused augmented reasoning with large language models [0.03%]
PKFAR：基于大型语言模型的精神病学知识融合增强推理方法
Rongzheng Wang,Cheng Yu,Qian Dong et al.
Rongzheng Wang et al.
Purpose: Psychiatric diagnosis faces significant challenges due to subjective symptom reporting and complex diagnostic criteria. While Large Language Models (LLMs) offer potential clinical decision support, their implemen...
ProsthetiX-AI: An LLM-based clinical decision support system for evidence-based prosthetic recommendations [0.03%]
ProsthetiX-AI：一种基于LLM的临床决策支持系统，用于循证假肢推荐
Vidyapati Kumar,Dilip Kumar Pratihar
Vidyapati Kumar
Prosthetic selection critically influences rehabilitation outcomes for lower-limb amputees, yet conventional approaches often rely on subjective clinical judgment and static protocols, frequently overlooking individualized patient factors. ...
Multi-omics data integration for disease phenotype prediction using adversarial vision graph networks [0.03%]
基于对抗视觉图网络的多组学数据集成及疾病表型预测方法研究
Ajni K Ajai
Ajni K Ajai
Disease Phenotype Prediction is the process of predicting the manifestation of specific observable features related to a disease based on genetic data. The primary challenge is to predict disease phenotypes accurately by integrating heterog...
Parameter-efficient fine-tuning with layer pruning on medical sequence-to-sequence modeling [0.03%]
基于分层剪枝的医学序列模型参数高效微调方法研究
Yunqi Zhu,Yuanyuan Wu,Wensheng Zhang et al.
Yunqi Zhu et al.
The increasing size of language models raises great research interests in parameter-efficient fine-tuning (PEFT) such as LoRA that freezes the main body of a pre-trained model, and injects small-scale trainable parameters for multiple downs...
UMEval: a unified framework for explainable medical term semantic evaluation with large language models [0.03%]
UMEval：一种基于大规模语言模型的解释性医学术语语义评估统一框架
Shuyu Liu,Linkun Feng,Youwei Luo et al.
Shuyu Liu et al.
Purpose: Accurate medical term semantic evaluation is critical for patient safety, timely diagnosis, and healthcare interoperability. Existing methods suffer from incomplete knowledge coverage, insufficient interpretabili...
Detecting stress from videos via intra-subject and inter-subject learning [0.03%]
基于个体间和个体内的学习的视频情感计算中压力检测方法研究
Yang Ding,Yi Dai,Lei Cao et al.
Yang Ding et al.
Mental stress poses a growing threat to public health, yet video based stress detection remains challenging because of substantial inter individual variability in physiological and expressive responses. To address this issue, we propose a n...
Enhancing LLM-based medical decision-making by test-time knowledge acquisition [0.03%]
通过测试时间知识获取增强基于LLM的医疗决策能力
Shipeng Li,Liuxin Bao,Shikun Li et al.
Shipeng Li et al.
Purpose: Medical decision-making (MDM) is a complex clinical reasoning process that requires the systematic integration of multidisciplinary knowledge and evidence. Current approaches based on large language models (LLMs)...
ConsTCM: aligning fundus images with constitution differentiation in multimodal language model for Traditional Chinese Medicine [0.03%]
基于多模态语言模型的中医体质辨识的眼底图像配准（ConsTCM）
Xiangyu Ji,Guozheng Rao,Pengwei Zhuang et al.
Xiangyu Ji et al.
Visual Language Models (VLMs) have shown significant potential in processing multimodal tasks in a wide range of domains, such as medical image understanding, comprehensive diagnosis, etc. In Traditional Chinese Medicine (TCM), VLMs have al...
DALI-Syn: integrating chemical LLMs with multi-level attention architectures for synergistic drug combination prediction [0.03%]
DALI-Syn：整合化学LLMs与多级注意力架构以预测协同药物组合
Huanhuan Du,Yayu Tian,Weiguang Wang et al.
Huanhuan Du et al.
Purpose: Predicting drug synergy remains a critical challenge in personalized cancer treatment. Existing computational methods have made significant progress, offering the potential to accelerate the discovery of novel dr...