Causal inference for time series datasets with partially overlapping variables [0.03%]
具有部分重叠变量的时间序列数据的因果推理
Louis Adedapo Gomez,Jan Claassen,Samantha Kleinberg
Louis Adedapo Gomez
Objective: Healthcare data provides a unique opportunity to learn causal relationships but the largest datasets, such as from hospitals or intensive care units, are often observational and do not standardize variables col...
Defining phenotypes of disease severity for long-term cardiovascular, renal, metabolic, and mental health conditions in primary care electronic health records: A mixed-methods study using the nominal group technique [0.03%]
基于电子健康记录的初级保健中长期心血管疾病、肾脏疾病、代谢性疾病和精神健康状况的疾病严重程度表型定义：使用名义小组技术的混合方法研究
Jennifer Cooper,Thomas Jackson,Shamil Haroon et al.
Jennifer Cooper et al.
Objective: Inclusion of severity measures for long-term conditions (LTC) could improve prediction models for multiple long-term conditions (MLTC) but some severity measures have limited availability in electronic health r...
ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images [0.03%]
基于类间位置先验的腰椎侧弯超声图像语义分割网络ICPPNet
Changlong Wang,You Zhou,Yuanshu Li et al.
Changlong Wang et al.
Objective: Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it...
RoBIn: A Transformer-based model for risk of bias inference with machine reading comprehension [0.03%]
基于Transformer的风险偏倚推理模型RoBIn：机器阅读理解方法
Abel Corrêa Dias,Viviane Pereira Moreira,João Luiz Dihl Comba
Abel Corrêa Dias
Objective: Scientific publications are essential for uncovering insights, testing new drugs, and informing healthcare policies. Evaluating the quality of these publications often involves assessing their Risk of Bias (RoB...
Benchmarking domain-specific pretrained language models to identify the best model for methodological rigor in clinical studies [0.03%]
基准测试领域特定的预训练语言模型以提高临床研究中的方法学严谨性
Fangwen Zhou,Rick Parrish,Muhammad Afzal et al.
Fangwen Zhou et al.
Objective: Encoder-only transformer-based language models have shown promise in automating critical appraisal of clinical literature. However, a comprehensive evaluation of the models for classifying the methodological ri...
A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification [0.03%]
一种新颖的机器学习工作流程，通过转录多标签特征和临床相关分类捕捉患者内部异质性
Silvia Cascianelli,Iva Milojkovic,Marco Masseroli
Silvia Cascianelli
Objectives: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based...
Low-cost algorithms for clinical notes phenotype classification to enhance epidemiological surveillance: A case study [0.03%]
低成本的临床记录表型分类算法在流行病学监测中的应用研究：一个案例研究报告
Javier Petri,Pilar Bárcena Barbeira,Martina Pesce et al.
Javier Petri et al.
Objective: Our study aims to enhance epidemic intelligence through event-based surveillance in an emerging pandemic context. We classified electronic health records (EHRs) from La Rioja, Argentina, focusing on predicting ...
Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction [0.03%]
基于表格到图像的迁移学习：心血管疾病预测的一个案例研究
Francisco J Lara-Abelenda,David Chushig-Muzo,Pablo Peiro-Corbacho et al.
Francisco J Lara-Abelenda et al.
Objective: Machine learning (ML) models have been extensively used for tabular data classification but recent works have been developed to transform tabular data into images, aiming to leverage the predictive performance ...
Multi-modal fusion model for Time-Varying medical Data: Addressing Long-Term dependencies and memory challenges in sequence fusion [0.03%]
一种时间变化的多模态医疗数据融合模型：处理序列融合中的长期依赖性和记忆问题挑战
Moxuan Ma,Muyu Wang,Lan Wei et al.
Moxuan Ma et al.
Background: Multi-modal time-varying data continuously generated during a patient's hospitalization reflects the patient's disease progression. Certain patient conditions may be associated with long-term states, which is ...
Deduplicating the FDA adverse event reporting system with a novel application of network-based grouping [0.03%]
基于网络分组的新型应用去重FDA不良事件报告系统
Kory Kreimeyer,Jonathan Spiker,Oanh Dang et al.
Kory Kreimeyer et al.
Objective: To improve the reliability of data mining for product safety concerns in the Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) by robustly identifying duplicate reports describing the ...