Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI [0.03%]
基于深度学习分割和MRI纹理分析的股骨头坏死塌陷预测研究
Shihua Gao,Haoran Zhu,Moshan Wen et al.
Shihua Gao et al.
Background: Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral ...
A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis [0.03%]
基于蛋白质-蛋白质相互作用网络的多重标准决策方法在诊断潜伏性结核感染中的应用研究
Somayeh Ayalvari,Marjan Kaedi,Mohammadreza Sehhati
Somayeh Ayalvari
Background: DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However,...
Katarina Gašperlin Stepančič,Ana Ramovš,Jože Ramovš et al.
Katarina Gašperlin Stepančič et al.
Background: Ageing is one of the most important challenges in our society. Evaluating how one is ageing is important in many aspects, from giving personalized recommendations to providing insight for long-term care eligib...
Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study [0.03%]
基于常规随访数据的机器学习方法预测肌萎缩侧索硬化症进展特征的临床事件——一项可行性研究
Alessandro Guazzo,Michele Atzeni,Elena Idi et al.
Alessandro Guazzo et al.
Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneou...
Pilot deployment of a machine-learning enhanced prediction of need for hemorrhage resuscitation after trauma - the ShockMatrix pilot study [0.03%]
增强预测创伤后出血复苏需求的机器学习模型的试点部署——ShockMatrix试点研究
Tobias Gauss,Jean-Denis Moyer,Clelia Colas et al.
Tobias Gauss et al.
Importance: Decision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation. ...
Predicting the onset of Alzheimer's disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995-2008) [0.03%]
基于电子健康记录预测阿尔茨海默病及其痴呆症状的出现：从_CACHE县记忆与老化研究_(1995—2008)获得的研究结果
Karen C Schliep,Jeffrey Thornhill,JoAnn T Tschanz et al.
Karen C Schliep et al.
Introduction: Clinical notes, biomarkers, and neuroimaging have proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We a...
Accelerated hazard prediction based on age time-scale for women diagnosed with breast cancer using a deep learning method [0.03%]
基于年龄时间尺度利用深度学习方法预测乳腺癌女性患者的加速危险度
Zahra Ramezani,Jamshid Yazdani Charati,Reza Alizadeh-Navaei et al.
Zahra Ramezani et al.
Breast cancer is the most common cancer in women. Previous studies have investigated estimating and predicting the proportional hazard rates and survival in breast cancer. This study deals with predicting accelerated hazards (AH) rate based...
Face and content validity of the EMPOWER-UP questionnaire: a generic measure of empowerment in relational decision-making and problem-solving [0.03%]
EMPOWER-UP问卷的面部和内容效度：关系决策和问题解决的通用赋权措施
Emilie Haarslev Schröder Marqvorsen,Line Lund,Sigrid Normann Biener et al.
Emilie Haarslev Schröder Marqvorsen et al.
Background: Decision-making and problem-solving processes are powerful activities occurring daily across all healthcare settings. Their empowering potential is seldom fully exploited, and they may even be perceived as dis...
Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review [0.03%]
用于预测手术过程中输血的机器学习模型综述性研究：构建相关模型的研究现状地图
Olivier Duranteau,Florian Blanchard,Benjamin Popoff et al.
Olivier Duranteau et al.
Massive transfusion of blood products poses challenges in determining the need for transfusion and the appropriate volume of blood products. This review explores the use of machine learning (ML) models to predict transfusion risk during sur...
Deep learning assisted cancer disease prediction from gene expression data using WT-GAN [0.03%]
基于WT-GAN的基因表达数据癌症疾病深度学习预测模型
U Ravindran,C Gunavathi
U Ravindran
Several diverse fields including the healthcare system and drug development sectors have benefited immensely through the adoption of deep learning (DL), which is a subset of artificial intelligence (AI) and machine learning (ML). Cancer mak...