How Does the Novel Coronavirus Kill? A Machine Learning Approach

2020 ◽  
Author(s):  
Logan Ryan ◽  
Huaqin Pan ◽  
Samson Mataraso ◽  
Anna Lynn-Palevsky ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
The Novel ◽  
Machine Learning Approach ◽  
Novel Coronavirus

scholarly journals Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain

2020 ◽  
Author(s):  
Amir Abdollahi ◽  
Maryam Rahbaralam
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Inverse Correlation ◽  
Effect Of Temperature ◽  
The Novel ◽  
Autonomous Communities ◽  
Infected People ◽  
Machine Learning Approach ◽  
Potential Factors ◽  
Novel Coronavirus

AbstractThe novel coronavirus (COVID-19) has already spread to almost every country in the world and has infected over 3 million people. To understand the transmission mechanism of this highly contagious virus, it is necessary to study the potential factors, including meteorological conditions. Here, we present a machine learning approach to study the effect of temperature, humidity and wind speed on the number of infected people in the three most populous autonomous communities in Spain. We find that there is a moderate inverse correlation between temperature and the daily number of infections. This correlation manifests for temperatures recorded up to 6 days before the onset, which corresponds well to the known mean incubation period of COVID-19. We also show that the correlation for humidity and wind speed is not significant.

scholarly journals Novel criteria to classify ARDS severity using a machine learning approach

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Mohammed Sayed ◽  
David Riaño ◽  
Jesús Villar
Keyword(s):  
Machine Learning ◽  
Distress Syndrome ◽  
Learning Approach ◽  
The Novel ◽  
Arterial Oxygenation ◽  
Positive End Expiratory Pressure ◽  
Machine Learning Approach ◽  
Mimic Iii ◽  
Over Time ◽  
Better Than

Abstract Background Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO2/(FiO2xPEEP) or P/FPE] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches. Methods We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time. Results P/FPE ratio outperformed PaO2/FiO2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745). Conclusions The novel P/FPE ratio to assess ARDS severity after onset over time is markedly better than current PaO2/FiO2 criteria. The use of P/FPE could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

scholarly journals A COMPARATIVE ANALYSIS OF COVID–19 BETWEEN INDIA AND OTHERS USING DATA MINING AND MACHINE LEARNING APPROACH

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
Kanika Bhalla ◽  
Ashish Kumar
Keyword(s):  
Machine Learning ◽  
Initial Phase ◽  
Respiratory Disorder ◽  
Learning Approach ◽  
Virus Spread ◽  
Global Pandemic ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Using Data ◽  
Novel Coronavirus

Novel coronavirus has caused a global pandemic which leads to acute respiratory disorder in humans. In this study, analysis of the transmission of communicable COVID19 disease in India is done. The machine learning model presents the comparison of India with other countries during initial phase of virus spread in India. After that its comparison with initial hard-hit countries is also done. Finally, we also performed time series analysis for prediction using prophet for the next seven days showing confirmed, recovered and deaths that will happen.

scholarly journals A Machine Learning Approach as an Aid for Early COVID-19 Detection

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4202
Author(s):  
Roberto Martinez-Velazquez ◽  
Diana P. Tobón V. ◽  
Alejandro Sanchez ◽  
Abdulmotaleb El Saddik ◽  
Emil Petriu
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
The Novel ◽  
Sensitivity Score ◽  
Machine Learning Approach ◽  
Physical Interactions ◽  
Learning Test ◽  
Novel Coronavirus ◽  
Specificity Score

The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document