An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation [0.03%]
基于Transformer的带权重图卷积网络注意力联合模型在药物不良反应关系抽取中的应用
Ed-Drissiya El-Allaly,Mourad Sarrouti,Noureddine En-Nahnahi et al.
Ed-Drissiya El-Allaly et al.
Adverse drug event (ADE) relation extraction is a crucial task for drug safety surveillance which aims to discover potential relations between ADE mentions from unstructured medical texts. To date, the graph convolutional networks (GCN) hav...
A weakly supervised model for the automated detection of adverse events using clinical notes [0.03%]
利用临床记录进行不良事件自动检测的弱监督模型
Josh Sanyal,Daniel Rubin,Imon Banerjee
Josh Sanyal
With clinical trials unable to detect all potential adverse reactions to drugs and medical devices prior to their release into the market, accurate post-market surveillance is critical to ensure their safety and efficacy. Electronic health ...
MRC4BioER: Joint extraction of biomedical entities and relations in the machine reading comprehension framework [0.03%]
MRC4BioER：在机器阅读理解框架中联合提取生物医学实体和关系
Cong Sun,Zhihao Yang,Lei Wang et al.
Cong Sun et al.
Extracting entities and their relations from unstructured literature to form structured triplets is essential for biomedical knowledge extraction. Because sentences in biomedical datasets usually have many special overlapping triplets, it i...
Bayesian tensor factorization-drive breast cancer subtyping by integrating multi-omics data [0.03%]
贝叶斯张量分解驱动的多组学数据整合及乳腺癌分型研究
Qian Liu,Bowen Cheng,Yongwon Jin et al.
Qian Liu et al.
Breast cancer is a highly heterogeneous disease. Subtyping the disease and identifying the genomic features driving these subtypes are critical for precision oncology for breast cancer. This study focuses on developing a new computational a...
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data [0.03%]
AutoScore-生存：利用右删失生存数据开发可解释的机器学习时间-到-事件评分系统
Feng Xie,Yilin Ning,Han Yuan et al.
Feng Xie et al.
Background: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually ...
Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types [0.03%]
分布外数据集中的类别不平衡问题：改进TextCNN以提高对罕见癌症类型的分类鲁棒性
Kevin De Angeli,Shang Gao,Ioana Danciu et al.
Kevin De Angeli et al.
In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasin...
Automatic detection of COVID-19 vaccine misinformation with graph link prediction [0.03%]
基于图链接预测的COVID-19疫苗错误信息自动检测
Maxwell A Weinzierl,Sanda M Harabagiu
Maxwell A Weinzierl
Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major ...
A cost-effective IoT learning environment for the training and assessment of surgical technical skills with visual learning analytics [0.03%]
一种用于通过可视化学习分析训练和评估外科技术技能的成本效益型物联网学习环境
Pablo Castillo-Segura,Carmen Fernández-Panadero,Carlos Alario-Hoyos et al.
Pablo Castillo-Segura et al.
Background: Surgeons need to train and certify their technical skills. This is usually done with the intervention of experts who monitor and assess trainees. Nevertheless, this is a time-consuming task that is subject to ...
Machine learning algorithm for feature space clustering of mixed data with missing information based on molecule similarity [0.03%]
基于分子相似性的含缺失信息的混合数据特征空间聚类机器学习算法
K Balaji
K Balaji
Clustering Algorithms have just fascinated significant devotion in machine learning applications owing to their great competence. Nevertheless, the existing algorithms quite have approximately disputes that need to be further deciphered. Fo...
PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes [0.03%]
PASCLex：一个从电子健康记录临床备注中得出的全面的新冠后急性症状（PASC）词汇表
Liqin Wang,Dinah Foer,Erin MacPhaul et al.
Liqin Wang et al.
Objective: To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research. ...