scholarly journals Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Jingmin Luo ◽  
Wei Zhang ◽  
Shiyang Tan ◽  
Lijue Liu ◽  
Yongping Bai ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Applied Research ◽  
Clinical Analysis ◽  
Rapid Progression ◽  
Human Beings ◽  
Integrated Diagnosis ◽  
Proposed Model ◽  
Diagnosis Rate ◽  
Artificial Intelligence Technology ◽  
Diagnosis Model

Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues.

scholarly journals Ischemic Stroke Prediction by Exploring Sleep Related Features

2021 ◽  
Vol 11 (5) ◽  
pp. 2083
Author(s):  
Jia Xie ◽  
Zhu Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xingshe Zhou
Keyword(s):  
Ischemic Stroke ◽  
Life Quality ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Neural System ◽  
Human Beings ◽  
Filter Model ◽  
Fine Grained ◽  
Proposed Model ◽  
Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

scholarly journals Effect of similar-to-me effect on job satisfaction and organizational trust

2017 ◽  
Vol 15 (4) ◽  
pp. 254-262
Author(s):  
Ardashir Zahed ◽  
Farzad Sattari Ardabili
Keyword(s):  
Job Satisfaction ◽  
Quantitative Data ◽  
Structural Equation ◽  
Applied Research ◽  
Organizational Trust ◽  
Strong Support ◽  
Public Organizations ◽  
Equation Modeling ◽  
Proposed Model ◽  
The Relationship

The present study intended to investigate the effect of managers’ similar-to-me bias on the job satisfaction and organizational trust between public organizations staff. The current study is a descriptive-correlational applied research with quantitative data collection (questionnaire). The results of structural equation modeling analyses conducted for 80 employees of Public organizations in Ardabil, Iran, offered strong support for the proposed model. The results indicated that there was a statistically significant relationship between similar-to-me effect and job satisfaction; furthermore, organizational trust mediated the relationship between similar-to-me effect and job satisfaction. It is worth noting that there was a positive relationship between organizational trust and job satisfaction.

Introduce the Quantitative Identification Method of Rolling Bearing in the Application of Fault Detection

2015 ◽  
Vol 742 ◽  
pp. 147-149
Author(s):  
Li Huo
Keyword(s):  
Rolling Bearing ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Support Vector ◽  
Decision Boundary ◽  
Proposed Model ◽  
Quantitative Identification ◽  
Diagnosis Model ◽  
Bearing Damage ◽  
Vector Description

Rolling bearing is an important part of rotating machinery. Its failure will directly affect the normal operation of the whole machinery. This study proposed an intelligent diagnosis model based on Fuzzy support vector description for the quantitative identification of bearing fault. The proposed model constructs the spherically shaped decision boundary by training the features of normal bearing data, and then calculates the fuzzy monitoring coefficient to identify the bearing damage.

scholarly journals Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

2019 ◽  
Vol 47 (6) ◽  
pp. 1400-1411 ◽  
Author(s):  
Kai Wang ◽  
Zhen Qiao ◽  
Xiaobin Zhao ◽  
Xiaotong Li ◽  
Xin Wang ◽  
...  
Keyword(s):  
Tumor Recurrence ◽  
Validation Cohort ◽  
Radiation Necrosis ◽  
Integrated Model ◽  
Magnetic Resonance Images ◽  
Diagnostic Model ◽  
Integrated Diagnosis ◽  
Diagnosis Model ◽  
Met Pet ◽  
Radiomics Signature

Abstract Purpose To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients. Methods Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), 11C-methionine (11C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients. Results The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of 18F-FDG, maximum of TBR of 11C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975–1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881–0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful. Conclusions Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.

High Resolution Screening of Genomic Aberrations in Follicular Lymphoma Using Microarray Based Comparative Genomic Hybridization (MATRIX-CGH).

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2271-2271
Author(s):  
Carsten Schwaenen ◽  
Swen Wessendorf ◽  
Andreas Viardot ◽  
Sandra Ruf ◽  
Martina Enz ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Clinical Data ◽  
Array Cgh ◽  
Screening Method ◽  
Comparative Genomic ◽  
Rapid Progression ◽  
Human Beings ◽  
Data Set ◽  
Term Survival ◽  
Genomic Aberrations

Abstract Follicular Lymphoma (FL), one of the most frequent lymphoma entities in the western world, is characterized by a highly variable clinical course reaching from rapid progression with fatal outcome to cases with long term survival. In a recent study applying chromosomal comparative hybridization (CGH) to FL, in 70% of the cases genomic aberrations were detectable and a loss of genomic material on chromosomal bands 6q25-q27 was the strongest predictor for short overall survival. However, limitations of CGH as a screening method are a restricted genomic resolution to 3–10 Mbp and demanding non-automated evaluation procedures. Thus, high throughput analysis of genomic alterations for risk adapted patient stratification and monitoring within treatment trials should rely on efficient and automated diagnostic techniques. In this study, we used array CGH to a novel generation of DNA Chips containing 2800 genomic DNA probes. Target clones comprised i) contigs mapping to genomic regions of possible pathogenetic relevance in lymphoma (n=610 target clones mapping to e.g. 1p, 2p, 3q, 7q, 9p, 11q, 12q, 13q, 17p, 18q, X); ii) selected oncogenes and tumor suppressor genes (n=686) potentially relevant in hematologic neoplasms; and iii) a large genome-wide cluster of 1502 target DNA clones covering the genome at a distance of app. 2 Mbp (part of the golden path clone set). This chip represents a median genomic resolution of app. 1.5 Mbp. In total, DNAs from 70 FL samples were analyzed and results were compared to data from chromosomal CGH experiments and clinical data sets. The sensitivity of array CGH was considerably higher compared to chromosomal CGH (aberrations in 95% of cases vs 70% of cases). Most frequent aberrations were gain mapping to chromosome arms 2p (21%), 7p (24%), 7q (30%), 12p (17%), 12q (21%), 18p (21%) and 18q (34%) as well as losses mapping to chromosome arms 1p (19%), 6q (23%), 7p (20%), 11q (26%) and 17p (20%). In addition, several genomic aberrations were identified which have not been described before in FL. Currently, these aberrations are characterized in more detail and results will be correlated with the clinical data set. Moreover, three recurrent sites of genomic polymorphisms in human beings affecting chromosomes 5q, 14q and 15q were identified. In conclusion, these data underline the potential of array CGH for the sensitive detection of genomic imbalances in FL.

Overview, Outstanding in the Field: Negotiating New Relationships in Applied Research

2008 ◽  
Vol 30 (3) ◽  
pp. 32-37
Author(s):  
Jeanne Simonelli
Keyword(s):  
Human Behavior ◽  
Applied Research ◽  
Human Beings ◽  
Rural Work ◽  
Changing World

When I first began studying anthropology, I thought the term fieldwork and the notion of field had to do with where we did our research. The image of remote and rural work sites was readily gleaned from reading classic ethnographies, and it wasn't until I read the term in context that I realized that field referred to the field of human behavior. As I learned more about the practice of anthropology, it became clear that the field was anywhere that human beings lived, worked and coped with the changing world around them.

Applied Research of Ant Colony Algorithm in Fault Diagnosis

2014 ◽  
Vol 1037 ◽  
pp. 353-356
Author(s):  
Shi Hong Bai ◽  
Li Rong Guang ◽  
Zhi Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Applied Research ◽  
Chemical Reactor ◽  
Production Equipment ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
High Quality ◽  
Diagnosis Model ◽  
Quality Approach

The ant colony optimization algorithm is applied in the fault diagnosis of the production equipment, on the basis of the study of it, the relative fault diagnosis model was established. According to the data of a kind of chemical reactor, the eight values of different state were extracted and used to train the fault diagnosis model. The result of the test is turned out to be accurate and the application of such a kind of algorithm is a high-quality approach to fault diagnosis.

Tolerance Analysis and Diagnosis Model of Compliant Block Assembly Considering Welding Deformation

2015 ◽  
Author(s):  
Junghyun Lee ◽  
Wooyoung Choi ◽  
Minseok Kang ◽  
Hyun Chung
Keyword(s):  
Tolerance Analysis ◽  
Normal Equation ◽  
Welding Distortion ◽  
Assembly Process ◽  
Accuracy Control ◽  
Mechanical Assembly ◽  
Multi Stage ◽  
Proposed Model ◽  
Diagnosis Model ◽  
Welding Processes

This paper proposes a simplified tolerance analysis and diagnosis model including the effects of welding distortion, for accuracy control in ship block assembly processes. The variation simulation model for tolerance analysis utilizes the concepts of the sources of variation and the compliant mechanical assembly model to include the welding distortions. The proposed model utilizes welding distortion patterns and a transformation matrix to efficiently model the deformation during the joining process. The diagnosis model assumes the multi-stage assemblies and that the variations of previous stages are propagated to the current stage. It calculates the sensitivity; a linear mapping from input parts to output assembly variations, and includes the effects of welding distortion as an additional vector that deviates the assembly variation further. The diagnosis model predicts the quantitative effect of each source of variations to the final assembly’s geometrical variation, based on normal equation and assembly stage’s state space equation model. The proposed model is applied to a realistic block assembly process for validation purpose. The model can effectively simulate the propagation of welding distortion as well as quantitatively identify variation patterns and welding processes throughout the multi-stage assembly process.

RFID-Enabled Object Relationship Identification

2012 ◽  
Vol 579 ◽  
pp. 416-426
Author(s):  
Jiang Liang Hou ◽  
Hsiu Hui Cheng ◽  
Hung Lung Lin
Keyword(s):  
Radio Frequency Identification ◽  
Guidance System ◽  
Human Beings ◽  
Simulated Environments ◽  
Proposed Model ◽  
Frequency Identification ◽  
And Performance ◽  
Relationship Identification ◽  
Object Interactions ◽  
Object Relationship

In the last decade, RFID (Radio Frequency Identification) technology has been applied in many applications to support the routine operations. These methods about RFID applications focus mainly on the specific issues in the application domains. In this study, a generic object relationship identification and guidance model is proposed to identify the relationships between objects (including human beings, physical goods and locations) and guide the objects toward their destination based on their predefined objectives via the RFID. Eight general types of object interactions including searching, blocking, no-in, no-out, conflict, intersection, coincidence, and support, are considered to identify object relationship and guide the objects. The proposed scheme consists of three modules namely object type combination (OTC), object relationship identification (ORI) and object guidance (OG). Moreover based on the proposed methodology, an Object Relationship Identification and Guidance System (ORIG-System) is developed and simulated environments are established in order to verify the feasibility and performance of the proposed model. As a whole, this study provides a methodology and system to provide effective support to the routine operations in distinct application domains via the RFID technology.

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