scholarly journals A machine learning model to predict death outcome in severe COVID-19 patients

2021 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Global Pandemic ◽  
Machine Learning Model ◽  
Tree Classifier ◽  
Novel Coronavirus

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

scholarly journals A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

2020 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Machine Learning Model ◽  
Tree Classifier

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

scholarly journals Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145968-145983 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Ataollah Shirzadi ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Farzaneh Sajedi Hosseini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Snow Avalanche ◽  
Random Subspace ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Tree Classifier

scholarly journals Covid-19 Analysis and Prediction using Data Science and Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 303-307
Author(s):  
Akshata Kulkarni
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Information Dissemination ◽  
Prediction Models ◽  
Learning Model ◽  
Future Trend ◽  
Control Measures ◽  
Machine Learning Model ◽  
Using Data ◽  
Novel Coronavirus

Abstract: Officials around the world are using several COVID-19 outbreak prediction models to make educated decisions and enact necessary control measures. In this study, we developed a Machine Learning model which predicts and forecasts the COVID-19 outbreak in India, with the goal of determining the best regression model for an in-depth examination of the novel coronavirus. Based on data available from January 31 to October 31, 2020, collected from Kaggle, this model predicts the number of confirmed cases in Maharashtra. We're using a Machine Learning model to foresee the future trend of these situations. The project has the potential to demonstrate the importance of information dissemination in improving response time and planning ahead of time to help reduce risk.

Machine learning algorithm for predicting longer postoperative length of stay among older cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21536-e21536 ◽  
Author(s):  
Armin Shahrokni ◽  
Seyed Ali Rokni ◽  
Hassan Ghasemzadeh
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Variable Selection ◽  
Cancer Patients ◽  
Learning Model ◽  
Selection Algorithm ◽  
Machine Learning Model ◽  
Tree Classifier ◽  
Postoperative Length ◽  
Older Cancer Patients

e21536 Background: To predict longer postoperative length of stay (LLOS) among older cancer patients, we have used a novel method to assess factors associated with this outcome. Methods: Our study was based on 47-item electronic Rapid Fitness Assessment (eRFA) data presented last year (Shahrokni, et al. (J Clin Oncol 34, 2016 (suppl; abstr 10011). LLOS was defined as LOS ≥8days.We used machine learning model to explore the relationship between > 70 pre, intra, and postoperative variables with LLOS. A filter-based variable selection algorithm was utilized to only identify the most important variables as defined by information gain. We computed the mutual information between each variable and the LOS, and ranked them from high to low gain. To create an interpretable machine learning model, we used decision tree classifier. The tree started with the most informative variable as the root and branches based on a threshold value such that the entropy decreases by most. Results: In a cohort of 492 postoperative older cancer patients (median age 80), the variable selection algorithm showed that only 5 variables are sufficient for predicting LLOS with an accuracy of 77.2%, sensitivity 81.6% and specificity 61.31% in all patients. The algorithm started with operation time (OT) of < or > 4 hrs. For example, patients with OT > 4 hours and social activity limitation (SAL) > 7 were 78% likely to have LLOS. Patients with SAL ≤ 7 and OT > 5 hours were 75% likely to have LLOS. Patients were 71% likely not to have LLOS if OT < 4 hours and SAL ≤ 7. To avoid the effect of potential data collection errors and increase the accuracy of the model, we used the Bootstrap Aggregating (bagging) method which ensembles the decision of a set small trees. Particularly, we generated 3 datasets from randomly sampling patients from current dataset with replacement and built 3 different trees. As a result of combining 3 short trees we reach the accuracy of 83.2% with sensitivity 83.9% and specificity 82.4%. Conclusions: Machine learning model could be helpful for predicting LLOS among older cancer patients. Prospective studies are needed to validate this method in different academic and community settings.

scholarly journals Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
High Risk ◽  
Decision Tree ◽  
Clinical Characteristics ◽  
Lactic Dehydrogenase ◽  
Decision Tree Model ◽  
Severe Illness ◽  
Tree Model ◽  
Risk Of Death ◽  
Male Patients ◽  
Novel Coronavirus

Abstract Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

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.

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