Post-Operative Infection Prediction and Risk Factor Analysis in Colorectal Surgery Using Data Mining Techniques: A Pilot Study

Azimi, Kamran; Honaker, Michael D; Madathil, Sreenath Chalil; Khasawneh, Mohammad T.

doi:10.1089/sur.2019.138

Cited by 10 publications

(18 citation statements)

References 30 publications

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“…22e24 The remaining articles were either regarding gastrectomy 25 or liver surgery. 26 The outcomes of the articles describing colorectal surgery were anastomotic leakage, 13 surgical site infection, 14,16 postoperative bleeding, 15 a combination of surgical site infection, bleeding, and ileus, 18 or any postoperative complication. 17 The outcomes of the articles describing bariatric surgery were anastomotic leakage and venous thromboembolism, 20 morbidity and mortality, 21 or any severe postoperative complication.…”

Section: Resultsmentioning

confidence: 99%

“…15,17,18,21,22 In the majority of the machine learning models tested, sensitivity was lower than specificity. 14,16,19,20,23,24,26 The lowest sensitivity was found in the study of Cao et al 19 with a value of 0.06 with a convolutional neural network. In the studies of Azimi et al, 14 Cao et al, 19 Grass et al, 16 and Nudel et al 20 sensitivities were below 0.50.…”

Section: Sensitivity Specificity Accuracy and Auc-rocmentioning

confidence: 89%

“…Ten studies did this by k-fold crossvalidation, either 5-fold or 10-fold. 12,15,16,18,19,21e23,25,26 Two articles used bootstrap resampling 14,17 to internally validate their data, and in 2 studies a training and validation split of the data was used without cross-validation. 20,24 The article of Adams et al 13 did not specify the type of internal validation.…”

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

“…In the article by Chen et al 15 it was not reported whether external validation of their machine learning model was performed. Data imbalance was handled in 6 studies, by preventing overfitting with the synthetic minority oversampling technique (SMOTE) 12,14,15,19 or the randomly oversampling examples (ROSE) method. 25 Nudel et al 20 handled data imbalance; however, they did not specify which method was used.…”

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

“…12,13,16e20,22e24,26 Three of the studies were at high risk of bias, 14,15,25 2 of them due or partly due to presence of potential confounders. 14,25 The study by Chen et al 15 was at high risk of bias in measurement of outcomes. The studies of Lu et al 25 and Wise et al 21 did not describe how many variables were missing and how they acted on missing data.…”

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

See 4 more Smart Citations

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Stam

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Ingwersen

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Section: Sensitivity Specificity Accuracy and Auc-rocmentioning

confidence: 89%

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

Section: Study Validity and Quality Assessmentmentioning

confidence: 99%

See 3 more Smart Citations

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Stam

Goedknegt

Ingwersen

et al. 2022

Surgery

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Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

et al. 2022

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Background Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided. Methods Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models. Results A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration. Conclusions Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

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Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

et al. 2023

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Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.

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Post-Operative Infection Prediction and Risk Factor Analysis in Colorectal Surgery Using Data Mining Techniques: A Pilot Study

Cited by 10 publications

References 30 publications

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

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