scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model (Preprint)

2020 ◽  
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

BACKGROUND COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

scholarly journals An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

10.2196/25442 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e25442
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Chul Park ◽  
Do Wan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Random Forest ◽  
Hospital Admission ◽  
Deep Neural Network ◽  
Care Providers ◽  
Blood Samples ◽  
Routine Blood ◽  
Forest Models ◽  
Random Forest Models

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

2020 ◽  
Vol 24 (16) ◽  
pp. 12079-12090 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Ravinesh C. Deo ◽  
Sungwon Kim ◽  
Mahsa Hasanpour Kashani ◽  
Vahid Karimi ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
River Flow ◽  
River Stage ◽  
Flow Prediction ◽  
Cascade Correlation ◽  
Forest Models ◽  
Random Forest Models

scholarly journals METAPHOR: Probability density estimation for machine learning based photometric redshifts

2016 ◽  
Vol 12 (S325) ◽  
pp. 197-200 ◽  
Author(s):  
V. Amaro ◽  
S. Cavuoti ◽  
M. Brescia ◽  
C. Vellucci ◽  
C. Tortora ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Probability Density Estimation ◽  
Photometric Redshifts ◽  
Validation Test ◽  
Fitting Method ◽  
Forest Models ◽  
Random Forest Models ◽  
Template Fitting

AbstractWe present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).

scholarly journals Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 99
Author(s):  
Galen Richardson ◽  
Sylvain G. Leblanc ◽  
Julie Lovitt ◽  
Krishan Rajaratnam ◽  
Wenjun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Spatial Patterns ◽  
Surface Texture ◽  
Limiting Factors ◽  
Forest Models ◽  
Random Forest Models ◽  
Relationship Of

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

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