Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods [0.03%]
基于无监督深度学习方法的医疗索赔程序代码滥用检测
Michael Suesserman,Samantha Gorny,Daniel Lasaga et al.
Michael Suesserman et al.
Background: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed ...
José Luis Varela-Aldás,Jorge Buele,Doris Pérez et al.
José Luis Varela-Aldás et al.
Background: Loss of cognitive and executive functions is a problem that affects people of all ages. That is why it is important to perform exercises for memory training and prevent early cognitive deterioration. The aim o...
A self-management app to improve asthma control in adults with limited health literacy: a mixed-method feasibility study [0.03%]
一种自我管理应用程序可改善成人哮喘患者的病情控制：一种混合方法可行性研究（文字理解有困难的地方，以技术资料为准）
Hani Salim,Ai Theng Cheong,Sazlina Sharif-Ghazali et al.
Hani Salim et al.
Background: Digital technology tailored for those with limited health literacy has the potential to reduce health inequalities. Although mobile apps can support self-management in chronic diseases, there is little evidenc...
CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images [0.03%]
基于深度学习的急性缺血性卒中多模态MRI图像病变分割框架
Yousef Gheibi,Kimia Shirini,Seyed Naser Razavi et al.
Yousef Gheibi et al.
Background: Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment ri...
Digital health literacy and digital engagement for people with severe mental ill health across the course of the COVID-19 pandemic in England [0.03%]
英格兰新冠大流行期间严重精神疾病患者的数字健康素养和数字参与度变化趋势研究
P Spanakis,B Lorimer,E Newbronner et al.
P Spanakis et al.
Background: An unprecedented acceleration in digital mental health services happened during the COVID-19 pandemic. However, people with severe mental ill health (SMI) might be at risk of digital exclusion, partly because ...
Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models [0.03%]
东非社区三国妇女儿童保健失访预测：机器学习模型的应用研究
Chenai Mlandu,Zvifadzo Matsena-Zingoni,Eustasius Musenge
Chenai Mlandu
Background: For optimal health, the maternal, newborn, and child healthcare (MNCH) continuum necessitates that the mother/child receive the full package of antenatal, intrapartum, and postnatal care. In sub-Saharan Africa...
SelANet: decision-assisting selective sleep apnea detection based on confidence score [0.03%]
基于置信度得分的自适应选择性睡眠呼吸暂停决策辅助检测方法
Beomjun Bark,Borum Nam,In Young Kim
Beomjun Bark
Background: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies...
Acquisition of temporal patterns from electronic health records: an application to multimorbid patients [0.03%]
从电子健康记录中获取时间模式：在多发病患者中的应用
Alicia Ageno,Neus Català,Marcel Pons
Alicia Ageno
Background: The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden pa...
The Infomóvel-An information system for managing HIV/AIDS patients in rural areas of Mozambique [0.03%]
Infomóvel-莫桑比克农村地区管理艾滋病患者的信息化管理系统
E Karajeanes,D Bila,M Luis et al.
E Karajeanes et al.
Background: Mobile health is gradually revolutionizing the way medical care is delivered worldwide. In Mozambique, a country with a high human immunodeficiency virus prevalence, where antiretroviral treatment coverage is ...
Observational Study
BMC medical informatics and decision making. 2023 Sep 18;23(1):187. DOI:10.1186/s12911-023-02281-6 2023
Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records [0.03%]
无监督、有监督和基于规则的学习的结合：在电子健康记录中检测患者的过敏情况的例子
Geir Thore Berge,Ole-Christoffer Granmo,Tor Oddbjørn Tveit et al.
Geir Thore Berge et al.
Background: Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learnin...