Tailoring task arithmetic to address bias in models trained on multi-institutional datasets [0.03%]
调整任务算术以解决多机构数据集训练模型中的偏见问题
Xiruo Ding,Zhecheng Sheng,Brian Hur et al.
Xiruo Ding et al.
Objective: Multi-institutional datasets are widely used for machine learning from clinical data, to increase dataset size and improve generalization. However, deep learning models in particular may learn to recognize the ...
An attention-based framework for integrating WSI and genomic data in cancer survival prediction [0.03%]
一种基于注意力的框架：整合全切片数字图像和基因组数据进行癌症生存预测
Genlang Chen,Sixuan Sui,Jiajian Zhang et al.
Genlang Chen et al.
Objective: Cancer survival prediction plays a vital role in enhancing medical decision-making and optimizing patient management. Accurate survival estimation enables healthcare providers to develop personalized treatment ...
Monitoring strategies for continuous evaluation of deployed clinical prediction models [0.03%]
临床预测模型连续评估的监测策略
Grace Y E Kim,Conor K Corbin,François Grolleau et al.
Grace Y E Kim et al.
Objective: As machine learning adoption in clinical practice continues to grow, deployed classifiers must be continuously monitored and updated (retrained) to protect against data drift that stems from inevitable changes,...
GRU-TV: Time- and Velocity-aware Gated Recurrent Unit for patient representation [0.03%]
基于时空门控递归单元的患者表征模型
Ningtao Liu,Shuiping Gou,Ruoxi Gao et al.
Ningtao Liu et al.
Objective: The multivariate clinical temporal series (MCTS) extracted from electronic health records (EHRs) can characterize the dynamic physiological processes. Previous deep patient representation models were proposed t...
Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction [0.03%]
基于多视图的异构图对比学习的药物-目标相互作用预测
Chao Li,Lichao Zhang,Guoyi Sun et al.
Chao Li et al.
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primari...
GatorCLR: Personalized predictions of patient outcomes on electronic health records using self-supervised contrastive graph representation [0.03%]
基于自监督对比图表示的电子健康记录上个性化预测患者预后效果(GatorCLR)
Yuxi Liu,Zhenhao Zhang,Jiacong Mi et al.
Yuxi Liu et al.
Objective: Recently, there has been growing interest in analyzing large amounts of Electronic Health Record (EHR) data. Patient outcome prediction is a major area of interest in EHR analysis that focuses on predicting the...
Focused digital cohort selection from social media using the metric backbone of biomedical knowledge graphs [0.03%]
基于生物医学知识图谱度量骨架的社交媒体数字人群聚焦式选择方法研究
Ziqi Guo,Jack Felag,Jordan C Rozum et al.
Ziqi Guo et al.
Social media data allows researchers to construct large digital cohorts-groups of users who post health-related content--to study the interplay between human behavior and medical treatment. Identifying the users most relevant to a specific ...
SigPhi-Med: A lightweight vision-language assistant for biomedicine [0.03%]
SigPhi-Med：一种轻量级的生物医学视觉语言助手
Feizhong Zhou,Xingyue Liu,Qiao Zeng et al.
Feizhong Zhou et al.
Background: Recent advancements in general multimodal large language models (MLLMs) have led to substantial improvements in the performance of biomedical MLLMs across diverse medical tasks, exhibiting significant transfor...
A trajectory-informed model for detecting drug-drug-host interaction from real-world data [0.03%]
一种用于从真实世界数据中检测药物-药物-宿主相互作用的轨迹信息模型
Yi Shi,Anna Sun,Hongmei Nan et al.
Yi Shi et al.
Objective: Adverse drug event (ADE) is a significant challenge to public health. Since data mining methods have been developed to identify signals of drug-drug interaction-induced (DDI-induced) or drug-host interaction-in...
Do it faster with PICOS: Generative AI-Assisted systematic review screening [0.03%]
借助生成式人工智能辅助的PICOS：更快地进行系统评价筛选
Sai Krishna Vallamchetla,Omar Abdelkader,Ali Elnaggar et al.
Sai Krishna Vallamchetla et al.
Background: Systematic reviews (SRs) require substantial time and human resources, especially during the screening phase. Large Language Models (LLMs) have shown the potential to expedite screening. However, their use in ...