Natural language generation in healthcare: A review of methods and applications [0.03%]
自然语言生成在医疗保健中的应用及方法研究综述
Mengxian Lyu,Xiaohan Li,Ziyi Chen et al.
Mengxian Lyu et al.
Objective: This study presents a systematic review of natural language generation (NLG) methods and applications in the medical domain, providing quantitative and qualitative analyses to answer four key research questions...
Learning dynamic binary treatment policies under treatment selection bias: A Conservative Q-Learning approach with representation balancing [0.03%]
具有治疗选择偏差下学习动态二元处理政策：一种保守的Q-学习方法和表示平衡
Animesh Kumar Paul,Russell Greiner
Animesh Kumar Paul
Objective: Learn safe, robust dynamic treatment regimes (DTRs) from observational trajectories that exhibit treatment selection bias, using an offline reinforcement learning (RL) approach. ...
Multi-channel causal variational autoencoder for multimodal biomedical causal disentanglement [0.03%]
多通道因果变分自编码器在多模态生物医学因果分离中的应用
Safaa Al-Ali,Irene Balelli;Alzheimer’s Disease Neuroimaging Initiative
Safaa Al-Ali
The multimodal nature of clinical assessment and decision-making, and the high rate of healthcare data generation, motivate the need to develop approaches specifically tailored to the analysis of these complex and potentially high-dimension...
KATMed: a knowledge-augmented transformer model for contraindication-aware medication recommendation in comorbidities [0.03%]
基于知识增强的变压器模型在并发疾病下的禁忌症aware药物推荐(KATMed)
Ziqian Qiao,Shaofu Lin,Jiatong Fan et al.
Ziqian Qiao et al.
Drug-disease contraindications in comorbidities (DDCC) pose a significant challenge and priority in clinical treatment. These contraindications exhibit a prototypical long-tail distribution, characterized by low-frequency, highly diverse, a...
Ontology-grounded knowledge graphs for mitigating hallucinations in large language models for clinical question answering [0.03%]
本体论支持的知识图谱在临床问答中用于缓解大型语言模型幻觉问题的研究
Mohamed Ali,Zaki Taha,Mohamed Mabrouk Morsey
Mohamed Ali
Objective: Large Language Models (LLMs) show strong potential in biomedical informatics but frequently generate hallucinated or factually incorrect responses, limiting their clinical utility. This study aims to develop an...
On the transfer learning behavior of domain-specific vision-language models in screening mammography [0.03%]
领域特定视觉语言模型在乳腺癌筛查中的迁移学习行为探究
Aisha Urooj Khan,Gokul Ramasamy,Muhammad Danish Khan et al.
Aisha Urooj Khan et al.
Vision-Language models have shown remarkable performance for natural images and text. Given the homology of the anatomy, high gray-scale image dimension, and the unbalanced datasets, the traditional VLMs do not adapt well to radiological ap...
A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review [0.03%]
一种多维分层框架：回顾现实世界医疗证据的偏倚来源
Haeun Lee,Christelle Xiong,Derek Baughman et al.
Haeun Lee et al.
Objective: This study identifies and categorizes bias sources throughout the real-world evidence (RWE) generation process from electronic health records (EHRs), and we develop a multi-dimensional conceptual framework to c...
Yingying Hou,Wenbin Yao,Xikang Zhu et al.
Yingying Hou et al.
Multimodal data plays a vital role in advancing personalized diagnosis and precision medicine. However, during cross-institutional sharing and collaborative analysis, the protection of patient privacy becomes increasingly critical, particul...
PEER: Towards reliable and efficient inference via Patience-Based Early Exiting with Rejection [0.03%]
一种基于忍耐力的早期退出模型加速方法
Zaifu Zhan,Shuang Zhou,Rui Zhang
Zaifu Zhan
Objective: In biomedical applications, models must balance inference efficiency with reliable predictions. Patience-based early exiting (PABEE) accelerates inference but often fails under uncertainty. ...
Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track [0.03%]
生物医学摘要简洁化 track 的教训
Brian Ondov,William Xia,Kush Attal et al.
Brian Ondov et al.
Objective: Recent advances in language models have shown potential to adapt professional-facing biomedical literature to plain language, making it accessible to patients and caregivers. However, their unpredictability and...