Predicting postoperative delirium after microvascular decompression surgery with machine learning

Wang, Ying; Lei, Lei; Ji, Mu‐Huo; Tong, Jianhua; Zhou, Chengmao; Yang, Jianjun

doi:10.1016/j.jclinane.2020.109896

Cited by 38 publications

(36 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies have also shown the advantages of using machine learning algorithms for prediction [ 27 ]. Lee et al used a deep learning method to predict bispectral index during target-controlled infusion of propofol and remifentanil [ 28 ] and showed that the concordance correlation coefficient was 0.561 in the deep learning model, significantly larger than that in the response surface model (0.265).…”

Section: Discussionmentioning

confidence: 99%

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

et al. 2021

View full text Add to dashboard Cite

Objective To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. Methods Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). Results For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. Conclusions In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…5,21,22 Use of multiple models and comparison of results has become more common in healthcare as investigators seek to overcome concerns around generalizability and accuracy of the results. 21,23 Although random forest has been shown to handle even imbalanced data, newer machine learning models such as CatBoost in addition have also demonstrated capability of handling different types of variables while maintaining high level of accuracy and efficiency. 8,24 Explainable and interpretable results from machine learning models may not demonstrate causality but provide better insight into the results of machine learning models, overcoming some of the concerns around machine learning being a black box.…”

Section: Discussionmentioning

confidence: 99%

Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

Mathur

Sethi

Mathur³

et al. 2020

Preprint

View full text Add to dashboard Cite

COVID-19 is now one of the leading causes of mortality amongst adults in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations. The goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel, and health care characteristics of different states across the United States and COVID-19 mortality. Using multiple public data sets, 24 variables linked to COVID-19 disease were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States. Feature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age,median income, climate and disease mitigation measures on COVID-19 related mortality remained unclear. COVID-19 policy making will need to take population density, pre-existing medical care and state travel policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

show abstract

“…The classification performance of the machine learning algorithms was evaluated using the statistical metrics of precision, recall, F1, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). F1 is a weighted average of precision and recall ( Wang et al, 2020 ), which can comprehensively evaluate the balance of model performance between precision and recall. A higher F1 value indicates a satisfactory performance of the model.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, machine learning could incorporate diversified neuroimaging features and identify critical factors or interactions that were previously unknown; this might improve the accuracy of the model ( Solomon et al, 2020 ). Due to the advantages, such as universality and accuracy, a large number of studies assessed the application of machine learning to rs-fMRI data for clinical diagnosis and prediction of neuropsychological disorders, such as AD and postoperative delirium ( Asgari et al, 2020 ; Wang et al, 2020 ; Zhu et al, 2021 ). However, no model has been established for the prediction of DNR using machine learning combined with neuroimaging data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

Jiang

Cai

Zhang

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

show abstract

Predicting postoperative delirium after microvascular decompression surgery with machine learning

Cited by 38 publications

References 21 publications

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

Contact Info

Product

Resources

About