Design, implementation and usability analysis of patient empowerment in ADLIFE project via patient reported outcome measures and shared decision making [0.03%]
基于患者报告结局和共同决策的ADLIFE项目中患者赋权的设计、实现及可用性分析
Gokce B Laleci Erturkmen,Natassia Kamilla Juul,Irati Erreguerena Redondo et al.
Gokce B Laleci Erturkmen et al.
Introduction: This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-...
Medical-informed machine learning: integrating prior knowledge into medical decision systems [0.03%]
医学信息机器学习：将先验知识融入医疗决策系统
Christel Sirocchi,Alessandro Bogliolo,Sara Montagna
Christel Sirocchi
Background: Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This ...
Operationalizing and digitizing person-centered daily functioning: a case for functionomics [0.03%]
面向个人的日常功能的操作和数字化：功能组学的案例分析
Esther R C Janssen,Ilona M Punt,Johan van Soest et al.
Esther R C Janssen et al.
An ever-increasing amount of data on a person's daily functioning is being collected, which holds information to revolutionize person-centered healthcare. However, the full potential of data on daily functioning cannot yet be exploited as i...
Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study [0.03%]
胰腺癌死亡风险预测模型的建立：一项回顾性研究
Raoof Nopour
Raoof Nopour
Background and aim: Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decre...
Addressing label noise for electronic health records: insights from computer vision for tabular data [0.03%]
面向电子健康记录的标签噪声处理：表格数据视角下的计算机视觉借鉴与思考
Jenny Yang,Hagen Triendl,Andrew A S Soltan et al.
Jenny Yang et al.
The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data ...
Development and usability testing of an online support tool to identify models and frameworks to inform implementation [0.03%]
一种在线支持工具的开发和可用性测试，以确定告知实施的模型和框架
Lisa Strifler,Christine Fahim,Michael P Hillmer et al.
Lisa Strifler et al.
Background: Theories, models and frameworks (TMFs) are useful when implementing, evaluating and sustaining healthcare evidence-based interventions. Yet it can be challenging to identify an appropriate TMF for an implement...
Patterns and factors associated with dental service utilization among insured people: a data mining approach [0.03%]
参保人群口腔医疗服务利用的行为特征及影响因素大数据分析
Zahra Pouraskari,Reza Yazdani,Maryam Khademi et al.
Zahra Pouraskari et al.
Background: Insurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective...
End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility [0.03%]
端到端的临床BERT模型微调后的伪匿名化：在保持数据效用的同时保护隐私
Thomas Vakili,Aron Henriksson,Hercules Dalianis
Thomas Vakili
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause...
Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction [0.03%]
电子病历疾病风险智能预测质量评价的定量指标体系构建
Jiayin Zhou,Jie Hao,Mingkun Tang et al.
Jiayin Zhou et al.
Objective: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). ...
A tree-based explainable AI model for early detection of Covid-19 using physiological data [0.03%]
基于树的可解释AI模型：使用生理数据进行COVID-19的早期检测
Manar Abu Talib,Yaman Afadar,Qassim Nasir et al.
Manar Abu Talib et al.
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 c...