scholarly journals Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

2022 ◽  
Author(s):  
Laith Abu Lekham ◽  
Yong Wang ◽  
Ellen Hey ◽  
Mohammad T. Khasawneh
Keyword(s):  
Rural Communities ◽  
Text Mining ◽  
Predictive Model ◽  
Test Results ◽  
Mining Model

scholarly journals 651 Longitudinal, unobtrusive, and ecologically valid sleep metric estimation from a smart bed to predict the pathology of COVID-19

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A255-A255
Author(s):  
Dmytro Guzenko ◽  
Gary Garcia ◽  
Farzad Siyahjani ◽  
Kevin Monette ◽  
Susan DeFranco ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Predictive Model ◽  
Sleep Duration ◽  
Wearable Sensors ◽  
Test Results ◽  
Cloud Data ◽  
Cardiorespiratory Function ◽  
Group Data ◽  
Rich Information

Abstract Introduction Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model. Methods An IRB approved survey was presented to opting-in users from August to November 2020. COVID-19 test results were reported by 2003/6878 respondents (116 positive; 1887 negative). From the positive group, data from 82 responders (44.7±11.3 yrs.) who reported the date of symptom onset were used. From the negative group, data from 1519 responders (48.4±12.9 yrs.) who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud. Data from January to October 2020 were considered. The predictive model consists of two levels: 1) the daily probability of staying healthy calculated by logistic regression and 2) a continuous density Hidden Markov Model to refine the daily prediction considering the past decision history. Results With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Evaluation of the predictive model resulted in cross-validated area under the receiving-operator curve (AUC) estimate of 0.84±0.09 which is similar to values reported for wearable-sensors. Considering additional days to confirm prediction improved the AUC estimate to 0.93±0.05. Conclusion The results obtained on the smart bed user population suggest that unobtrusive sleep metrics may offer rich information to predict and track the development of symptoms in individuals infected with COVID-19. Support (if any):

Design Considerations for Flow Noise in Automotive Air Handling Systems

1999 ◽  
Author(s):  
David W. Warner ◽  
Niranjan G. Humbad ◽  
Basem Alzahabi ◽  
Robert A. Porada
Keyword(s):  
Pressure Drop ◽  
Predictive Model ◽  
Design Process ◽  
Heat Exchangers ◽  
Test Results ◽  
Noise Levels ◽  
Flow Noise ◽  
Design Considerations ◽  
System Pressure ◽  
Noise Data

Abstract Noise from automotive air handling systems is an important issue for driver and passenger comfort. This study was undertaken to quantify the flow noise from the blower and remaining system, and to develop an analytical predictive model for airflow noise. Tests were conducted on four different vehicle Air Handling Systems (AHS) comprised of blower, heat exchangers, ducts and panel registers. Flow and noise data were measured. Test results suggest that overall noise is dominated by blower noise. A predictive model for airflow noise was developed. This model suggests system pressure drop (Δp) and system airflow (q) being dominant parameters in the noise predictions. The noise variation scales as flow velocity to the power 5.75. The developed model for flow noise can be very useful in the design process to estimate noise levels for new systems from CFD/CAE analyses.

scholarly journals Analyzing the Relationship between Consumer Satisfaction and Fresh E-Commerce Logistics Service Using Text Mining Techniques

2019 ◽  
Vol 11 (13) ◽  
pp. 3570 ◽  
Author(s):  
Wei Hong ◽  
Changyuan Zheng ◽  
Linhai Wu ◽  
Xujin Pu
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Consumer Satisfaction ◽  
Rapid Development ◽  
Digital Information ◽  
Logistics Service ◽  
Communication Reliability ◽  
Fresh Products ◽  
Mining Model ◽  
The Relationship

The rapid development of the Internet and the transformation of consumption patterns have prompted consumers to purchase fresh products online. For fresh e-commerce enterprises, logistics is an important aspect of customer satisfaction. Therefore, this study focused on online review information and used a convolutional neural network text mining model for its analysis. Logistics service elements concerned with customer satisfaction are convenience, communication, integrity, responsiveness, and reliability. Thereafter, comment information was converted to digital information using sentiment analysis. Finally, a correlation analysis was carried out to compare the significance of various influencing factors. The results confirm that convenience, communication, reliability, and responsiveness had a significant impact on customer satisfaction, whereas integrity had none. Fresh e-commerce logistic services need to improve for the development of the companies.

A Processing Predictive Model of Ultrasonic Vibration Grinding Assisted Electric Discharge Machining Based on Support Vector Machines

2014 ◽  
Vol 941-944 ◽  
pp. 1928-1931
Author(s):  
Guang Mei Yang ◽  
Yun Peng Zhang ◽  
Kai Yue Li ◽  
Guo Ding Chen
Keyword(s):  
Predictive Model ◽  
Ultrasonic Vibration ◽  
Electric Discharge ◽  
Orthogonal Test ◽  
Support Vector ◽  
Maximum Relative Error ◽  
Test Results ◽  
Electric Discharge Machining ◽  
Good Accord ◽  
Vector Machines

To predict the machining results of ultrasonic vibration grinding assisted electric discharge machining (UVGAEDM) in the condition of building predictive model with a few samples but fluctuant values, a predictive model based on SVM was proposed in this paper. Taking machining SiCp/Al as an example, the samples for modeling were obtained through orthogonal test, and then the predictive model was established utilizing MATLAB. Finally, the model was optimized to further improve the prediction accuracy about the processing indicatorssurface roughness and processing velocity. It shows that the predictive results are in good accord with the test results, with the maximum relative error being less than 12%, meaning the predictive model is reliable and effective.

scholarly journals Analisis Penggunaan Siskeudes dalam Pengelolaan Dana Desa

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Desy Nur Pratiwi ◽  
Yuwita Ariessa Pravasanti
Keyword(s):  
Rural Communities ◽  
Behavioral Intention ◽  
Perceived Usefulness ◽  
Primary Data ◽  
Intention To Use ◽  
Test Results ◽  
Sample Collection ◽  
Simultaneous Test ◽  
The Government ◽  
Behavioral Intention To Use

The government provides village funds to improve the welfare of rural communities and equitable development. The purpose of this study was to examine internal and external factors that influence the use of Siskeudes. This study uses primary data in the form of questionnaires and distributed to village fund managers in eight districts in Sukoharjo Regency. The sample collection technique in this study uses convenience sampling method and the total number of samples collected is 32 villages. Hypothesis testing uses multiple linear regression. Partial test results indicate that the variable usefulness (perceived usefulness) and interest in using technology (behavioral intention to use) affect the use of Siskeudes. Simultaneous test results show that the perceived usefulness (perceived usefulness) and interest in using technology (behavioral intention to use) together affect the use of Siskeudes. Keywords: behavioral intention to use, perceived usefulness and Siskeudes

scholarly journals Application Value of Rapid Predictive Model for Readmission Risk in Patients after CABG

2020 ◽  
Vol 23 (5) ◽  
pp. E668-E672
Author(s):  
Tiao Lv ◽  
Yinghong Zhang ◽  
Wen Zhang ◽  
Liu Hu ◽  
Guozhen Liu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Roc Curve ◽  
Test Results ◽  
Good Discrimination ◽  
C Statistic ◽  
Lemeshow Test ◽  
Area Under Roc Curve ◽  
Cabg Patients ◽  
Readmission Risk ◽  
Heart Hospital

Objective: To explore the value of a rapid risk predictive model for the readmission of patients after CABG in China. Methods: The rapid predictive model of readmission risk was translated into Chinese, and then validated with data from 758 patients who underwent CABG in Wuhan Asian Heart Hospital from January 2018 to June 2019. The discrimination was tested by area under the ROC curve (AUC), and the calibration was tested by Hosmer-Lemeshow test. Results: The rapid risk predictive model for readmission showed good discrimination and calibration in Chinese CABG patients (The area under ROC curve c-statistic: 0.704, 95% CI: 0.614-0.794; Hosmer-Lemeshow test: P = .955). Conclusion: The rapid readmission risk predictive model can be used in Chinese CABG patients soon after admission.

Text Mining to Define a Validated Model of Hospital Rankings

2008 ◽  
pp. 268-296
Author(s):  
Patricia Bintzler Cerrito
Keyword(s):  
Text Mining ◽  
Predictive Model ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Categorical Data ◽  
Kernel Density ◽  
Categorical Variable ◽  
Text String

The purpose of this chapter is to demonstrate how text mining can be used to reduce the number of levels in a categorical variable to then use the variable in a predictive model. The method works particularly well when several levels of the variable have the same identifier so that they can be combined into a text string of variables. The stemming property of the linked words is used to create clusters of these strings. In this chapter, we validate the technique through kernel density estimation, and we compare this technique to other techniques used to reduce the levels of categorical data.

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