Associations between blood pressure control and clinical events suggestive of nutrition care documented in electronic health records of patients with hypertension [0.03%]
高血压患者血压控制与电子健康记录中营养护理相关临床事件之间的关联性研究
April R Williams,Maria D Thomson,Erin L Britton
April R Williams
Background: Clinical events suggestive of nutrition care found in electronic health records (EHRs) are rarely explored for their associations with hypertension outcomes. ...
Prostate cancer detection using e-nose and AI for high probability assessment [0.03%]
基于电子鼻和AI的前列腺癌检测以评估高概率癌症发生的可能性
J B Talens,J Pelegri-Sebastia,T Sogorb et al.
J B Talens et al.
This research aims to develop a diagnostic tool that can quickly and accurately detect prostate cancer using electronic nose technology and a neural network trained on a dataset of urine samples from patients diagnosed with both prostate ca...
Research on the doctors' win in crowdsourcing competitions: perspectives on service content and competitive environment [0.03%]
关于众包竞赛中医生胜出的研究——服务内容与竞争环境的视角
Xiuxiu Zhou,Shanshan Guo,Hong Wu
Xiuxiu Zhou
Medical crowdsourcing competitions can help patients get more efficient and comprehensive treatment advice than "one-to-one" service, and doctors should be encouraged to actively participate. In the crowdsourcing competitions, winning the c...
Development and validation of a case definition for problematic menopause in primary care electronic medical records [0.03%]
绝经期障碍在基层医疗电子病例中的定义的发展与验证
Anh N Q Pham,Michael Cummings,Nese Yuksel et al.
Anh N Q Pham et al.
Background: Menopause is a normal transition in a woman's life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. This study develope...
Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning [0.03%]
从日语转录采访中提取痴呆患者和护理者的潜在需求：Z分数在机器学习中用作词素选择的输入数据的可用性初步评估
Nanae Tanemura,Tsuyoshi Sasaki,Ryotaro Miyamoto et al.
Nanae Tanemura et al.
Background: Given the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involve...
Smartphone-based application to control and prevent overweight and obesity in children: design and evaluation [0.03%]
基于智能手机的应用程序可控制和防止儿童肥胖：设计与评估
Zahra Zare,Elmira Hajizadeh,Maryam Mahmoodi et al.
Zahra Zare et al.
Background: Obesity is a multifaceted condition that impacts individuals across various age, racial, and socioeconomic demographics, hence rendering them susceptible to a range of health complications and an increased ris...
Development of a machine learning-based acuity score prediction model for virtual care settings [0.03%]
基于机器学习的虚拟护理环境急性评分预测模型的研发
Justin N Hall,Ron Galaev,Marina Gavrilov et al.
Justin N Hall et al.
Objective: Healthcare is increasingly digitized, yet remote and automated machine learning (ML) triage prediction systems for virtual urgent care use remain limited. The Canadian Triage and Acuity Scale (CTAS) is the gold...
Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles [0.03%]
监督机器学习在根据肌肉分类运动诱发电位方面的机遇与挑战
Jonathan Wermelinger,Qendresa Parduzi,Murat Sariyar et al.
Jonathan Wermelinger et al.
Background: Even for an experienced neurophysiologist, it is challenging to look at a single graph of an unlabeled motor evoked potential (MEP) and identify the corresponding muscle. We demonstrate that supervised machine...
Design and development of a mobile-based self-care application for patients with depression and anxiety disorders [0.03%]
基于移动设备的抑郁和焦虑障碍患者自我管理应用的设计与开发
Khadijeh Moulaei,Kambiz Bahaadinbeigy,Esmat Mashoof et al.
Khadijeh Moulaei et al.
Background and aim: Depression and anxiety can cause social, behavioral, occupational, and functional impairments if not controlled and managed. Mobile-based self-care applications can play an essential and effective role...
Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study [0.03%]
基于舌象和肿瘤标志物的非小细胞肺癌不同分期的机器学习预测模型：一项初步研究
Yulin Shi,Hao Wang,Xinghua Yao et al.
Yulin Shi et al.
Objective: To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at dif...