scholarly journals A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

10.2196/21628 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e21628
Author(s):  
Shan Nan ◽  
Tianhua Tang ◽  
Hongshuo Feng ◽  
Yijie Wang ◽  
Mengyang Li ◽  
...  
Keyword(s):  
Data Model ◽  
Rapid Development ◽  
Treatment Guideline ◽  
Diagnosis And Treatment ◽  
Data Set ◽  
The Public ◽  
Global Pandemic ◽  
Public Data ◽  
Treatment Knowledge ◽  
Clinical Rules

Background COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

scholarly journals A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination (Preprint)

2020 ◽  
Author(s):  
Shan Nan ◽  
Tianhua Tang ◽  
Hongshuo Feng ◽  
Yijie Wang ◽  
Mengyang Li ◽  
...  
Keyword(s):  
Data Model ◽  
Rapid Development ◽  
Treatment Guideline ◽  
Diagnosis And Treatment ◽  
Data Set ◽  
The Public ◽  
Global Pandemic ◽  
Public Data ◽  
Treatment Knowledge ◽  
Clinical Rules

BACKGROUND COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. OBJECTIVE The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. METHODS A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. RESULTS We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. CONCLUSIONS Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

scholarly journals CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes

2019 ◽  
Vol 18 ◽  
pp. 117693511989029
Author(s):  
James LT Dalgleish ◽  
Yonghong Wang ◽  
Jack Zhu ◽  
Paul S Meltzer
Keyword(s):  
Copy Number ◽  
High Performance ◽  
Data Sets ◽  
Data Set ◽  
The Public ◽  
Public Data ◽  
Analysis Package ◽  
Cis And Trans ◽  
High Performance Computing Cluster ◽  
Shiny Application

Motivation: DNA copy number (CN) data are a fast-growing source of information used in basic and translational cancer research. Most CN segmentation data are presented without regard to the relationship between chromosomal regions. We offer both a toolkit to help scientists without programming experience visually explore the CN interactome and a package that constructs CN interactomes from publicly available data sets. Results: The CNVScope visualization, based on a publicly available neuroblastoma CN data set, clearly displays a distinct CN interaction in the region of the MYCN, a canonical frequent amplicon target in this cancer. Exploration of the data rapidly identified cis and trans events, including a strong anticorrelation between 11q loss and17q gain with the region of 11q loss bounded by the cell cycle regulator CCND1. Availability: The shiny application is readily available for use at http://cnvscope.nci.nih.gov/ , and the package can be downloaded from CRAN ( https://cran.r-project.org/package=CNVScope ), where help pages and vignettes are located. A newer version is available on the GitHub site ( https://github.com/jamesdalg/CNVScope/ ), which features an animated tutorial. The CNVScope package can be locally installed using instructions on the GitHub site for Windows and Macintosh systems. This CN analysis package also runs on a linux high-performance computing cluster, with options for multinode and multiprocessor analysis of CN variant data. The shiny application can be started using a single command (which will automatically install the public data package).

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We perform the numerical analysis using the stochastic differential equation in Itô’s calculus (SDE) for the estimating of novel cases daily as well as analytical calculations solving the correspondent Fokker-Planck equation for the density probability distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô diffusion fits well to the results due to uncertain in the official data and to the number of tests realized in the populations of each country.

An end-to-End deep context gate convolutional visual odometry system based on lightweight attention mechanism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yan Xu ◽  
Hong Qin ◽  
Jiani Huang ◽  
Yanyun Wang
Keyword(s):  
Visual Odometry ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Global Features ◽  
Data Set ◽  
Content Type ◽  
Generalization Ability ◽  
The Public ◽  
Public Data ◽  
End To End

Purpose Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability. Design/methodology/approach Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system. Findings The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively. Originality/value The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals Fall Detection with CNN-Casual LSTM Network

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 403
Author(s):  
Jiang Wu ◽  
Jiale Wang ◽  
Ao Zhan ◽  
Chengyu Wu
Keyword(s):  
Fall Detection ◽  
Detection Algorithm ◽  
Personal Privacy ◽  
Data Set ◽  
The Public ◽  
The Neural Network ◽  
Public Data ◽  
Spatio Temporal ◽  
Axis Rotation ◽  
Lstm Network

Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.

scholarly journals SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2181
Author(s):  
Junyan Li ◽  
Kang Yin ◽  
Chengpei Tang
Keyword(s):  
Activity Recognition ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Original Data ◽  
Data Sets ◽  
Data Set ◽  
The Public ◽  
Data Processing Method ◽  
Public Data ◽  
Multiple Samples

Currently, there are various works presented in the literature regarding the activity recognition based on WiFi. We observe that existing public data sets do not have enough data. In this work, we present a data augmentation method called window slicing. By slicing the original data, we get multiple samples for one raw datum. As a result, the size of the data set can be increased. On the basis of the experiments performed on a public data set and our collected data set, we observe that the proposed method assists in improving the results. It is notable that, on the public data set, the activity recognition accuracy improves from 88.13% to 97.12%. Similarly, the recognition accuracy is also improved for the data set collected in this work. Although the proposed method is simple, it effectively enhances the recognition accuracy. It is a general channel state information (CSI) data augmentation method. In addition, the proposed method demonstrates good interpretability.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
Probability Density Distribution ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô’s calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker-Planck equation for the probability density distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô’s diffusion fits well to the results due to uncertainty in the official data and to the number of testsrealized in populations of each country.

Sign in / Sign up

Export Citation Format

Share Document