Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts [0.03%]
利用机器学习预测临床决策支持的依从性、减少警报负担并评估重复实验室检测订购警报的效果
Jason M Baron,Richard Huang,Dustin McEvoy et al.
Jason M Baron et al.
Objectives: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and ...
Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning [0.03%]
基于搜索引擎和机器学习的医院病历与死亡证明关联研究
Sebastien Cossin,Serigne Diouf,Romain Griffier et al.
Sebastien Cossin et al.
Introduction: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly avail...
Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout [0.03%]
衡量成功：三个优化项目的视角，在职业倦怠的时代评估影响的观点
Eli M Lourie,Lindsay A Stevens,Emily C Webber
Eli M Lourie
Electronic health record (EHR) optimization has been identified as a best practice to reduce burnout and improve user satisfaction; however, measuring success can be challenging. The goal of this manuscript is to describe the limitations of...
Organizational characteristics and perceptions of clinical event notification services in healthcare settings: a study of health information exchange [0.03%]
医疗卫生场所组织特征与临床事件通报服务认知：卫生信息交换的研究视角
Kevin K Wiley,Katy Ellis Hilts,Jessica S Ancker et al.
Kevin K Wiley et al.
Objective: Event notification systems are an approach to health information exchange (HIE) that notifies end-users of patient interactions with the healthcare system through real-time automated alerts. We examined associa...
Modeling physician variability to prioritize relevant medical record information [0.03%]
通过模拟医生的差异性来优先处理相关的医疗记录信息
Mohammadamin Tajgardoon,Gregory F Cooper,Andrew J King et al.
Mohammadamin Tajgardoon et al.
Objective: Patient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. Howe...
Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data [0.03%]
基于人群队列和电子健康记录数据的2型糖尿病患者心血管疾病风险预测模型的建立及验证研究
Jackie Szymonifka,Sarah Conderino,Christine Cigolle et al.
Jackie Szymonifka et al.
Objective: Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-base...
Machine learning identifies girls with central precocious puberty based on multisource data [0.03%]
基于多源数据的机器学习鉴别中枢性早熟女童
Liyan Pan,Guangjian Liu,Xiaojian Mao et al.
Liyan Pan et al.
Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, ...
Spot the difference: comparing results of analyses from real patient data and synthetic derivatives [0.03%]
发现差异：利用真实患者数据和合成派生数据的分析结果进行比较
Randi E Foraker,Sean C Yu,Aditi Gupta et al.
Randi E Foraker et al.
Background: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic deri...
Framework for identifying drug repurposing candidates from observational healthcare data [0.03%]
基于观察性医疗数据的药物再利用候选物识别框架
Michal Ozery-Flato,Yaara Goldschmidt,Oded Shaham et al.
Michal Ozery-Flato et al.
Objective: Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information o...
Robert P Seifert,Vektra Casler,Nada Al Qaysi et al.
Robert P Seifert et al.
Laboratory Information Systems (LIS) and data visualization techniques have untapped potential in anatomic pathology laboratories. Pre-built functionalities of LIS do not address all the needs of a modern histology laboratory. For instance,...
Case Reports
JAMIA open. 2020 Nov 30;3(4):530-535. DOI:10.1093/jamiaopen/ooaa066 2020