scholarly journals Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

2020 ◽  
Author(s):  
KOFFKA KHAN ◽  
Emilie Ramsahai
Keyword(s):  
Machine Learning ◽  
Reporting Rate ◽  
Machine Learning Techniques ◽  
Patient Treatment ◽  
Third Wave ◽  
The United Kingdom ◽  
Learning Techniques ◽  
And Gender ◽  
Novel Coronavirus ◽  
The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to aect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of 2019-nCoV over 'early' datasets. A major consideration is the likelihood of death for patients with 2019-nCoV.Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classiers) on two 2019-nCoVdatasets obtained from Kaggle on March 30, 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. We included the patient's 'disease' history (only present in the second dataset) to predict the outcome for the second dataset.Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than 7-fold. The models ignoring a patent's 'disease' history performed poorly in test predictions.Conclusion: Our ndings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive eect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

scholarly journals Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koffka Khan ◽  
Emilie Ramsahai
Keyword(s):  
Machine Learning ◽  
Reporting Rate ◽  
Machine Learning Techniques ◽  
Patient Treatment ◽  
Third Wave ◽  
The United Kingdom ◽  
Learning Techniques ◽  
And Gender ◽  
Novel Coronavirus ◽  
The Impact

Abstract Background An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome (‘recovered’, ‘isolated’ or ‘death’) risk estimates of 2019-nCoV over ‘early’ datasets. A major consideration is the likelihood of death for patients with 2019-nCoV. Method Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used ‘country’, ‘age’ and ‘gender’ as features to predict outcome for both datasets. We included the patient’s ‘disease’ history (only present in the second dataset) to predict the outcome for the second dataset. Results The use of a patient’s ‘disease’ history improves the prediction of ‘death’ by more than sevenfold. The models ignoring a patent’s ‘disease’ history performed poorly in test predictions. Conclusion Our findings indicate the potential of using a patient’s ‘disease’ history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

scholarly journals Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

2021 ◽  
Author(s):  
KOFFKA KHAN ◽  
Emilie Ramsahai
Keyword(s):  
Machine Learning ◽  
Reporting Rate ◽  
Machine Learning Techniques ◽  
Patient Treatment ◽  
Third Wave ◽  
The United Kingdom ◽  
Learning Techniques ◽  
And Gender ◽  
Novel Coronavirus ◽  
The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome (’recovered’, ’isolated’ or ’death’) risk estimates of 2019-nCoV over ’early’ datasets. A major consideration is the likelihood of death for patients with 2019-nCoV.Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest Neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used ’country’, ’age’ and ’gender’ as features to predict outcome for both datasets. We included the patient’s ’disease’ history (only present in the second dataset) to predict the outcome for the second dataset.Results: The use of a patient’s ’disease’ history improves the prediction of ’death’ by more than 7-fold. The models ignoring a patent’s ’disease’ history performed poorly in test predictions.Conclusion: Our findings indicate the potential of using a patient’s ’disease’ history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

scholarly journals Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

2020 ◽  
Author(s):  
KOFFKA KHAN ◽  
Emilie Ramsahai
Keyword(s):  
Machine Learning ◽  
Reporting Rate ◽  
Machine Learning Techniques ◽  
Patient Treatment ◽  
Third Wave ◽  
The United Kingdom ◽  
Learning Techniques ◽  
And Gender ◽  
Novel Coronavirus ◽  
The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV? Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset. Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions. Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals Machine learning approach for simulation of heavy metal concentration in river water: the Crimean peninsula case study

2020 ◽  
Vol 163 ◽  
pp. 06009
Author(s):  
Evgeniy Malygin ◽  
Mikhail Lychagin
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Heavy Metal Concentration ◽  
Machine Learning Techniques ◽  
Crimean Peninsula ◽  
River Waters ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The Impact

This study proposes an approach for simulation of heavy metal concentration in river waters using machine learning techniques. A regression model was built and it captured the relationship between the concentration of heavy metal and metalloids (HMM) and several characteristics of studied catchment. Machine learning techniques allowed to simulate the annual concentration variability of HMM. This approach allows exploring the impact of different factors on studied processes.

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