Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery

Xue, Qingyun; Duan, Wen; Ji, Mu‐Huo; Tong, Jianhua; Yang, Jianjun; Zhou, Chengmao

doi:10.3389/fmed.2021.655686

Cited by 16 publications

(11 citation statements)

References 34 publications

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“…The risk prediction model constructed by random forest algorithm was then verified in the training dataset and testing dataset, respectively. Logistic regression is a linear fit of a response variable to a logarithmic probability ratio ( 15 , 16 ). The aim of classification by logistic regression is to establish a regression formula to classify boundary lines based on existing data.…”

Section: Methodsmentioning

confidence: 99%

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

et al. 2022

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BackgroundPost-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients.MethodsThis retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model.ResultsFeature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively.ConclusionsCompared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.

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Section: Methodsmentioning

confidence: 99%

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

et al. 2022

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“…In a clinical setting, correctly identifying patients who are at risk of PPCs is critical, so a model with high sensitivity is appropriate ( 34 ). Previous studies on predicting PPCs have achieved sensitivities in the range of 0.321–0.526 ( 4 , 18 , 19 ). Compared with previous studies and other models in our study, the deep neural network model achieved the highest sensitivity of 0.603, which indicated that the deep neural network model based on combined natural language data and structured data could more accurately identify patients with PPCs.…”

Section: Discussionmentioning

confidence: 99%

“…Geriatric patients are at a high risk of developing PPCs (9). In other studies (17)(18)(19), the data of older and younger patients have often been pooled together. Considering age-related physiological characteristics, ignoring age categories can cause inaccurate parameter estimation (21) and may decrease the discrimination ability in geriatric patients.…”

Section: Discussionmentioning

confidence: 99%

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A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model

et al. 2022

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AimPostoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.MethodsWe consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.ResultsThe derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889).ConclusionsThis study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.

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“…Presently, many intubation difficulties in thyroid surgery patients cannot be predicted in advance, due to limited predictive tools (2,3). Machine learning has been applied to several medical fields, including cancer, pulmonary complications, chronic pain, and mental health (4)(5)(6)(7). A study of patients with obesity has demonstrated that machine learning can help predict difficult intubations: Among the six machine learning algorithms, only three can predict intubation difficulty in patients with obesity, and the Xgbc algorithm has the best comprehensive performance, with an accuracy rate exceeding 80% (8).…”

Section: Introductionmentioning

confidence: 99%

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms

Zhou

Wang

Xue

et al. 2022

Front. Public Health

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BackgroundIn this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.MethodsWe used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python.ResultsThe top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%.ConclusionAccording to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.

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Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery

Cited by 16 publications

References 34 publications

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms

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