Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

Karajizadeh, Mehrdad; Nasiri, Mahdi; Yadollahi, Mahnaz; Zolfaghari, Amir Hussain; Pakdam, Ali

doi:10.4258/hir.2020.26.4.284

Cited by 9 publications

(10 citation statements)

References 22 publications

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“…In addition, renal injuries accounted for a small percentage of trauma injuries in this study; therefore, we were unable to describe more variables in different subgroups, such as patients with various degrees of injuries. It is suggested to use data mining and modeling methods to predict the outcome of renal trauma patients (17,28).…”

Section: Discussionmentioning

confidence: 99%

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Pattern and Severity of Traumatic Renal Injury: A Three-Year Study at Single Level One Trauma Center in Southern Iran

2023

ircmj

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Background: Renal trauma is among the most important trauma challenges. Better management of traumatic renal patients is necessary to improve the patients’ clinical outcome. Objectives: This study aimed to analyze the pattern, severity, mechanism of injury, and outcome in renal trauma. Methods: This cross-sectional study was conducted in the largest trauma center in southern Iran from March 2018 to June 2022. Adult patients with diagnosed renal trauma based on abbreviated injury scale guideline were included. Variables of age, gender, anatomy of injury, mechanism of injury, lenghth of hospitalization, level of blood pressure, severity of injury, and the outcome of patients have been collected. Results: In total, 4,416 traumatic patients were admitted to the Emergency Department of the Hospital during the study time, of which 46 cases had traumatic renal injury. The rate of renal injury in the level one trauma center in southern Iran was 0.96%, and the death rate in renal injury victims was 12(26.1%). Most of the injured with renal injury were men 39(84.8%). Blunt trauma was the dominant type of trauma in most victims with renal injury 34(73.9%). Thirty-seven percent (n=17) of traumatic renal injury victim was in mild level (grade 1 and 2), and 16(34.8%) in severe level (grade 4 and 5). Renal injury was mostly associated with thorax injury (n=46) as extra-abdominal organ injury, and liver injury (n=16) and spleen (n=15) as intra-abdominal organ injury. Conclusion: The results of the present study showed that the severity of injury in patients with renal injury is high, and it is usually associated by injury to other body organs. It is suggested that the traumatic patients should be immediately examined for renal injury in the resuscitation department due to the high mortality rate and severity of these patients.

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Section: Discussionmentioning

confidence: 99%

“…The abbreviated injury scale was determined according to our previous study (17,18). To calculate an ISS, we squared each AIS code in each of the three most severely affected ISS body regions and added them together (ISS=A 2 + B 2 + C 2 where A, B, C are the AIS scores of the three most injured ISS body regions).…”

Section: Methodsmentioning

confidence: 99%

Pattern and Severity of Traumatic Renal Injury: A Three-Year Study at Single Level One Trauma Center in Southern Iran

2023

ircmj

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“…The ADASYN technique generates more realistic points deviated from the line by producing random points and adding random noise and it is a recently developed improved version of SMOTE. There have been continuous attempts to develop advanced algorithms that have better accuracy than SMOTE [42].…”

Section: Discussionmentioning

confidence: 99%

“…SMOTE can alleviate the overfitting problem due to random oversampling and has the advantage of not losing useful data compared to undersampling or oversampling techniques [40]. However, it has also been reported that SMOTE may cause class overlapping, induce additional noise, and not be effective for treating imbalanced data with a high-dimensional y variable [42]. Therefore, although this study confirmed the effectiveness of SMOTE using an imbalanced binary dataset, the results cannot be generalized for all dimensions of data and the result should be interpreted with caution.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Byeon¹

2021

IJACSA

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Since the number of healthy people is much more than that of ill people, it is highly likely that the problem of imbalanced data will occur when predicting the depression of the elderly living in the community using big data. When raw data are directly analyzed without using supplementary techniques such as a sample algorithm for datasets, which have imbalanced class ratios, it can decrease the performance of machine learning by causing prediction errors in the analysis process. Therefore, it is necessary to use a data sampling technique for overcoming this imbalanced data issue. As a result, this study tried to identify an effective way for processing imbalanced data to develop ensemble-based machine learning by comparing the performance of sampling methods using the depression data of the elderly living in South Korean communities, which had quite imbalanced class ratios. This study developed a model for predicting the depression of the elderly living in the community using a logistic regression model, gradient boosting machine (GBM), and random forest, and compared the accuracy, sensitivity, and specificity of them to evaluate the prediction performance of them. This study analyzed 4,085 elderly people (≥60 years old) living in the community. The depression data of the elderly in the community used in this study had an unbalance issue: the result of the depression screening test showed that 87.5% of subjects did not have depression, while 12.5% of them had depression. This study used oversampling, undersampling, and SMOTE methods to overcome the unbalance problem of the binary dataset, and the prediction performance (accuracy, sensitivity, and specificity) of each sampling method was compared. The results of this study confirmed that the SMOTE-based random forest algorithm showing the highest accuracy (a sensitivity ≥ 0.6 and a specificity ≥ 0.6) was best prediction performance among random forest, GBM, and logistic regression analysis. Further studies are needed to compare the accuracy of SMOTE, undersampling, and oversampling for imbalanced data with high dimensional y-variables.

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“…To avoid overestimating the performance of the model, an imbalanced data set should be treated carefully when training a supervised classification machine learning model [ 18 , 19 ]. Along with accuracy, we wanted to interpret the performance of the model using indicators such as precision, recall, F1 score, AUPRC, and no information rate.…”

Section: Discussionmentioning

confidence: 99%

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Shin¹,

Park²,

Lee³

et al. 2021

JMIR Med Inform

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Background Myocardial injury after noncardiac surgery (MINS) is associated with increased postoperative mortality, but the relevant perioperative factors that contribute to the mortality of patients with MINS have not been fully evaluated. Objective To establish a comprehensive body of knowledge relating to patients with MINS, we researched the best performing predictive model based on machine learning algorithms. Methods Using clinical data from 7629 patients with MINS from the clinical data warehouse, we evaluated 8 machine learning algorithms for accuracy, precision, recall, F1 score, area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve to investigate the best model for predicting mortality. Feature importance and Shapley Additive Explanations values were analyzed to explain the role of each clinical factor in patients with MINS. Results Extreme gradient boosting outperformed the other models. The model showed an AUROC of 0.923 (95% CI 0.916-0.930). The AUROC of the model did not decrease in the test data set (0.894, 95% CI 0.86-0.922; P=.06). Antiplatelet drugs prescription, elevated C-reactive protein level, and beta blocker prescription were associated with reduced 30-day mortality. Conclusions Predicting the mortality of patients with MINS was shown to be feasible using machine learning. By analyzing the impact of predictors, markers that should be cautiously monitored by clinicians may be identified.

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Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

Cited by 9 publications

References 22 publications

Pattern and Severity of Traumatic Renal Injury: A Three-Year Study at Single Level One Trauma Center in Southern Iran

Pattern and Severity of Traumatic Renal Injury: A Three-Year Study at Single Level One Trauma Center in Southern Iran

Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

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