Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data

Cho, Hunyong; Matthews, Gregory J.; Harel, Ofer

doi:10.1111/insr.12277

Cited by 17 publications

(11 citation statements)

References 44 publications

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“…Furthermore, the Receiver Operating Characteristics (ROC) curve is another important tool for analyzing the diagnostic performance of a binary classifier. ROC curve gives the trade-off between sensitivity and specificity as the threshold varies [29]. One important feature of ROC curve is the Area Under the Curve (AUC) which provides the overall summary of the classifier's performance.…”

Section: Results From Off-the-shelf Features and Svmmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Section: Results From Off-the-shelf Features and Svmmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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“…The RF model improves the prediction accuracy of the model by aggregating a large number of classification regression trees, which can be used to solve classification and regression problems [30]. The sample dataset of each tree is generated by bootstrap resampling technology.…”

Section: Rf Modelmentioning

confidence: 99%

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

et al. 2019

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With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau.

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“…The threshold with the maximum Youden index was chosen as the best threshold. Acceptable discrimination is represented by an area under the curve of 0.70-0.79, good discrimination by an area ≥0.80, and excellent discrimination by an area ≥0.90 (10,11).…”

Section: Discussionmentioning

confidence: 99%

“…The threshold with the maximum Youden index was chosen as the best threshold. Acceptable discrimination is represented by an area under the curve of 0.70–0.79, good discrimination by an area ≥0.80, and excellent discrimination by an area ≥0.90 ( 10 , 11 ). The Hosmer–Lemeshow goodness-of-fit test was used to evaluate calibration or the degree of agreement between the predicted and observed mortality assessed using the PRISM III, P-MODS and PELOD-2 scoring systems.…”

Section: Methodsmentioning

confidence: 99%

Performance of PRISM III, PELOD-2, and P-MODS Scores in Two Pediatric Intensive Care Units in China

Zhang

Huang

et al. 2021

Front. Pediatr.

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Objective: The performances of the pediatric risk of mortality score III (PRISM III), pediatric logistic organ dysfunction score-2 (PELOD-2), and pediatric multiple organ dysfunction score (P-MODS) in Chinese patients are unclear. This study aimed to assess the performances of these scores in predicting mortality in critically ill pediatric patients.Methods: This retrospective observational study was conducted at two tertiary-care PICUs of teaching hospitals in China. A total of 1,253 critically ill pediatric patients admitted to the two Pediatric Intensive Care Units (PICUs) of the First Affiliated Hospital, Sun Yat-Sen University from August 2014 to December 2019 and Shen-Zhen Children's Hospital from January 2019 to December 2019 were analyzed. The indexes of discrimination and calibration were applied to evaluate score performance for the three models (PRISM III, PELOD-2, and P-MODS scores). The receiver operating characteristic (ROC) curve was plotted, and the efficiency of PRISM III, PELOD-2, and P-MODS in predicting death were evaluated by the area under ROC curve (AUC). Hosmer–Lemeshow goodness-of-fit test was used to evaluate the degree of fitting between the mortality predictions of each scoring system and the actual mortality.Results: A total of 1,253 pediatric patients were eventually enrolled in this study (median age, 38 months; overall mortality rate, 8.9%; median length of PICU stay, 8 days). Compared to the survival group, the non-survival group showed significantly higher PRISM III, PELOD-2, and P-MODS scores [PRISM III: 18 (12, 23) vs. 11 (0, 16); PELOD-2, 8 (4, 10) vs. 4 (0, 6); and P-MODS: 5 (4, 9) vs. 3 (0, 4), all P < 0.001]. ROC curve analysis showed that the AUCs of PRISM III, PELOD-2, and P-MODS for predicting the death of critically ill children were 0.858, 0.721, and 0.596, respectively. Furthermore, in the Hosmer–Lemeshow goodness-of-fit test, PRISM III and PELOD-2 showed the better calibration between predicted mortality and observed mortality (PRISM III: χ2 = 5.667, P = 0.368; PELOD-2: χ2 = 9.582, P = 0.276; P-MODS: χ2 = 12.449, P = 0.015).Conclusions: PRISM III and PELOD-2 can discriminate well between survivors and non-survivors. PRISM III and PELOD-2 showed the better calibration between predicted and observed mortality, while P-MODS showed poor calibration.

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Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data

Cited by 17 publications

References 44 publications

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

Performance of PRISM III, PELOD-2, and P-MODS Scores in Two Pediatric Intensive Care Units in China

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