Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting [0.03%]
评估临床乳腺癌环境下患者预后预测的模型选择框架的可识别性
C M Phillips,E A B F Lima,C Wu et al.
C M Phillips et al.
We develop a family of mathematical models to predict patient-specific response to neoadjuvant therapy in breast cancer. The models capture key features of tumor growth, therapeutic response, and tissue mechanics that are informed by diffus...
Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition [0.03%]
基于 proper orthogonal decomposition 的乳腺癌化疗反应预测模型快速标定
Chase Christenson,Chengyue Wu,David A Hormuth nd et al.
Chase Christenson et al.
Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption. In this work, we demonstrate that proper orthogonal decomposition,...
Dory: Computation of persistence diagrams up to dimension two for Vietoris-Rips filtrations of large data sets [0.03%]
Dory：计算Vietoris-Rips滤链的大数据集的二维持续图
Manu Aggarwal,Vipul Periwal
Manu Aggarwal
Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various ...
Coefficient identification in a SIS fractional-order modelling of economic losses in the propagation of COVID-19 [0.03%]
基于SIS分数阶模型的COVID-19经济损失系数识别问题研究
Slavi G Georgiev,Lubin G Vulkov
Slavi G Georgiev
A fractional-order SIS (Susceptible-Infectious-Susceptible) model with time-dependent coefficients is used to analyse some effects of the novel coronavirus 2019 (COVID-19). This generalized model is suitable for describing the COVID dynamic...
Proper Orthogonal Decomposition Methods for the Analysis of Real-Time Data: Exploring Peak Clustering in a Secondhand Smoke Exposure Intervention [0.03%]
实时数据的 Proper Orthogonal 分解方法分析：探索二手烟暴露干预中的峰值聚类现象
V Berardi,R Carretero-González,N E Klepeis et al.
V Berardi et al.
This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in ho...
D-Cov19Net: A DNN based COVID-19 detection system using lung sound [0.03%]
D-Cov19Net：使用肺音的基于DNN的COVID-19检测系统
Sukanya Chatterjee,Jishnu Roychowdhury,Anilesh Dey
Sukanya Chatterjee
The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution N...
Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images [0.03%]
基于学习扩充的鲁棒性深卷积神经网络在含噪X光图像中检测COVID-19的方法
Adel Akbarimajd,Nicolas Hoertel,Mohammad Arafat Hussain et al.
Adel Akbarimajd et al.
Deep convolutional neural networks (CNNs) are used for the detection of COVID-19 in X-ray images. The detection performance of deep CNNs may be reduced by noisy X-ray images. To improve the robustness of a deep CNN against impulse noise, we...
Timing the race of vaccination, new variants, and relaxing restrictions during COVID-19 pandemic [0.03%]
新冠肺炎大流行期间疫苗接种、新变异毒株和限制放宽的竞赛及其影响分析
Carolina Ribeiro Xavier,Rafael Sachetto Oliveira,Vinícius da Fonseca Vieira et al.
Carolina Ribeiro Xavier et al.
Late in 2019, China identified a new type of coronavirus, SARS-CoV-2, and due to its fast spread, the World Health Organisation (WHO) declared a pandemic named COVID-19. Some variants of this virus were detected, including the Delta, which ...
Pedro Cárdenas,Ioannis Ivrissimtzis,Boguslaw Obara et al.
Pedro Cárdenas et al.
The COVID-19 epidemic has changed the world dramatically since societies are changing their behaviour according to the new normal, which comes along with numerous challenges and uncertainties. These uncertainties have led to instabilities i...
Computational analysis of cardiac structure and function in congenital heart disease: Translating discoveries to clinical strategies [0.03%]
计算分析先天性心脏病的心脏结构和功能：将发现转化为临床策略
Nickolas Forsch,Sachin Govil,James C Perry et al.
Nickolas Forsch et al.
Increased availability and access to medical image data has enabled more quantitative approaches to clinical diagnosis, prognosis, and treatment planning for congenital heart disease. Here we present an overview of long-term clinical manage...