scholarly journals Predicting Risk of Mortality in Pediatric ICU Based on Ensemble Step-Wise Feature Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shenda Hong ◽  
Xinlin Hou ◽  
Jin Jing ◽  
Wendong Ge ◽  
Luxia Zhang
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Mortality Prediction ◽  
Ranking And Selection ◽  
Data Driven ◽  
Feature Ranking ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pediatric Icu ◽  
Logistic Regression Classifier

Background. Prediction of mortality risk in intensive care units (ICU) is an important task. Data-driven methods such as scoring systems, machine learning methods, and deep learning methods have been investigated for a long time. However, few data-driven methods are specially developed for pediatric ICU. In this paper, we aim to amend this gap—build a simple yet effective linear machine learning model from a number of hand-crafted features for mortality prediction in pediatric ICU. Methods. We use a recently released publicly available pediatric ICU dataset named pediatric intensive care (PIC) from Children’s Hospital of Zhejiang University School of Medicine in China. Unlike previous sophisticated machine learning methods, we want our method to keep simple that can be easily understood by clinical staffs. Thus, an ensemble step-wise feature ranking and selection method is proposed to select a small subset of effective features from the entire feature set. A logistic regression classifier is built upon selected features for mortality prediction. Results. The final predictive linear model with 11 features achieves a 0.7531 ROC-AUC score on the hold-out test set, which is comparable with a logistic regression classifier using all 397 features (0.7610 ROC-AUC score) and is higher than the existing well known pediatric mortality risk scorer PRISM III (0.6895 ROC-AUC score). Conclusions. Our method improves feature ranking and selection by utilizing an ensemble method while keeping a simple linear form of the predictive model and therefore achieves better generalizability and performance on mortality prediction in pediatric ICU.

Estimation of data-driven streamflow predicting models using machine learning methods

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Tanveer Ahmed Siddiqi ◽  
Saima Ashraf ◽  
Sadiq Ali Khan ◽  
Muhammad Jawed Iqbal
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Predicting rice blast disease: machine learning versus process-based models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
David F. Nettleton ◽  
Dimitrios Katsantonis ◽  
Argyris Kalaitzidis ◽  
Natasa Sarafijanovic-Djukic ◽  
Pau Puigdollers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rice Blast ◽  
Machine Learning Algorithms ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Plant Disease Management ◽  
Process Based Models

Abstract Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

scholarly journals Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1866 ◽  
Author(s):  
Liao ◽  
Wang ◽  
Zhang ◽  
Abbod ◽  
Shih ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Pseudomonas Aeruginosa ◽  
Intensive Care ◽  
Prediction Models ◽  
Support Vector ◽  
Ventilator Associated Pneumonia ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pseudomonas Aeruginosa Infection

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models’ performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

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