Designing an mHealth App for Defecatory Function Rehabilitation of Post-Surgical Colorectal Cancer Survivors [0.03%]
结直肠癌术后幸存者排便功能康复的移动健康应用程序设计
Seoyeon Park,Jiyeong Hong,Dong-Hyuk Park et al.
Seoyeon Park et al.
Many colorectal cancer (CRC) survivors who have undergone resection surgery experience persistent bowel dysfunction that significantly affects their quality of life, highlighting the need for defecatory function rehabilitation in survivorsh...
A Literature Review on Example-Based Explanations in Medical Image Analysis [0.03%]
基于实例解释在医学图像分析中的文献综述
Helena Montenegro,Jaime S Cardoso
Helena Montenegro
Deep learning has been extensively applied to medical imaging tasks over the past years, achieving outstanding results. However, the obscure reasoning of the models and the lack of supportive evidence causes both clinicians and patients to ...
A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives [0.03%]
基于语言模型的生物医学研究和应用中合成数据生成的范围审查：数据效用和质量视角
Hanshu Rao,Weisi Liu,Haohan Wang et al.
Hanshu Rao et al.
Synthetic data generation using large language models (LLMs) demonstrates substantial promise in addressing biomedical data challenges and shows increasing adoption in biomedical research. This study systematically reviews recent advances i...
Towards Accurate and Reliable ICU Outcome Prediction: A Multimodal Learning Framework Based on Belief Function Theory using Structured EHRs and Free-Text Notes [0.03%]
基于结构化EHR和自由文本笔记的直觉主义信任函数理论多模态学习框架在ICU预后预测中的应用研究
Yucheng Ruan,Daniel J Tan,See-Kiong Ng et al.
Yucheng Ruan et al.
Accurate Intensive Care Unit (ICU) outcome prediction is critical for improving patient treatment quality and ICU resource allocation. Existing research mainly focuses on structured data, e.g. demographics and vital signs, and lacks effecti...
Jacob Thrasher,Alina Devkota,Prasiddha Siwakoti et al.
Jacob Thrasher et al.
Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progresse...
A Hybrid Language Framework for Ontology-Based Clinical Concept Extraction [0.03%]
基于本体的临床概念抽取的混合语言框架
Behnaz Eslami,Dmitriy Dligach,Nazanin Azarvash et al.
Behnaz Eslami et al.
This study presents a hybrid ontology-based framework for clinical concept extraction from narrative EHR discharge summaries using large language models (LLMs) and standardized biomedical terminologies. The framework integrates multiple NLP...
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries [0.03%]
大型语言模型在医学查询中受用户驱动因素的影响的敏感性
Kyung Ho Lim,Ujin Kang,Xiang Li et al.
Kyung Ho Lim et al.
Large language models (LLMs) are increasingly used in healthcare; however, their reliability is shaped not only by model design but also by how queries are phrased and how complete the information is. This study assesses how user-driven fac...
Imputation Free Deep Survival Prediction with Conditional Variational Autoencoders [0.03%]
基于条件变分自编码器的无插补深度生存预测方法
Natalia Hong,Aditya Acharya,Krishna Gokhale et al.
Natalia Hong et al.
Electronic Health Records (EHRs) provide rich opportunities for developing risk prediction tools to support clinical decision-making, yet they are inherently incomplete because data are recorded selectively during routine care. Such missing...
A Vision-based System for Monitoring Eating Behaviors and Musculoskeletal Function [0.03%]
一种用于监测进食行为和肌骨功能的视觉系统
Muhammad Ahmed Raza,Robert B Fisher
Muhammad Ahmed Raza
Camera-based systems offer a comprehensive and inconspicuous approach to monitoring the well-being of individuals within the comfort of their homes. This study introduces a vision-based, fully autonomous pipeline for assessing eating behavi...
Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records [0.03%]
基于电子健康记录的图模型在阿尔茨海默病表型识别中的应用研究
Yu Huang,Jie Xu,Zhengkang Fan et al.
Yu Huang et al.
Understanding the heterogeneity of neurodegeneration in Alzheimer's disease (AD) and identifying distinct progression pathways is critical for improving diagnosis, treatment, prognosis, and prevention. Motivated by this need, this study aim...