Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease

Jiang, Hangjin; Mao, Haifeng; Lu, Huimin; Pan, Lin; Garry, Wei; Lu, Huijing; Yang, Guangqian; Rainer, Timothy; Chen, Xiaohui

doi:10.1016/j.ijmedinf.2020.104326

Cited by 43 publications

(24 citation statements)

References 35 publications

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“…One‐hot encoding for categorical variables includes T stage, N stage, gender, race, laterality, year of diagnosis, histological type, and sex. For instance, grade features with four values can be described as [(1000, 0100, 0010, 0001)] 19 …”

Section: Methodsmentioning

confidence: 99%

“…XGBoost, previously used to predict the association of miRNA diseases, is a machine learning algorithm implemented under the gradient boosting framework 23 . The RF, which can be used to decrease training variance and improve integration and generalization, refers to a machine learning classifier that uses multiple trees to train and predict samples 19 . The KNN is one of the most widely used nonparametric classification methods, which is based on the belief that if most of the k‐nearest samples in the vicinity of a sample belong to a specific class in the feature space, the sample also belongs to this category 24 .…”

Section: Methodsmentioning

confidence: 99%

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Prediction of lung metastases in thyroid cancer using machine learning based on SEER database

Liu

Wang

et al. 2022

Cancer Medicine

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Purpose Lung metastasis (LM) is one of the most frequent distant metastases of thyroid cancer (TC). This study aimed to develop a machine learning algorithm model to predict lung metastasis of thyroid cancer for providing relative information in clinical decision‐making. Methods Data comprising of demographic and clinicopathological characteristics of patients with thyroid cancer were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, which is employed to develop six machine learning algorithm models support vector machine (SVM), logistic regression (LR), eXtreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), and k‐nearest neighbor (KNN). Compared and evaluated models by the following indicators: accuracy, precision, recall rate, F1‐score, the area under the ROC curve (AUC) value and Brier score, and interpreted the association between clinicopathological characteristics and target variables based on the best model. Results Nine thousand nine hundred and fifty patients were selected, which including 212 patients (2.1%) with lung metastasis, and 9738 patients without lung metastasis (97.9%). Multivariate logistic regression showed that age, T stage, N stage, and histological type were independent factors in TC with LM. Evaluation indicators of the best model‐ RF were as following: accuracy (0.99), recall rate (0.88), precision (0.61), F1‐score (0.72), AUC value (0.99), and the Brier score (0.016). Conclusion RF learning model performed better and can be applied to forecast lung metastasis of thyroid cancer, and offer valuable and significant reference for clinicians' decision‐making in advance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of lung metastases in thyroid cancer using machine learning based on SEER database

Liu

Wang

et al. 2022

Cancer Medicine

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“…The output result of XGBoost is calculated according to the result of the leaf node of each CART. Compared with other traditional machine learning models (LR, DT, or RF), XGBoost is an integrated algorithm based on a tree model, which not only can deal with the problem of data sparsity, but also can learn the nonlinear relationships between features, so as to improve its generalization ability and robustness [25,26]. Compared with other models, the XGBoost model has better identification ability and better goodness-of-fit.…”

Section: Discussionmentioning

confidence: 99%

A machine learning predictive model of in-hospital mortality in patients with sepsis complicated by anemia: a retrospective study based on the MIMIC-III database

Liu

Zhao

Qin³

et al. 2021

Preprint

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BackgroudPatients with sepsis complicated by anemia have a higher risk of mortality. It is clinically important to study the risk factors associated with the prognosis of this disease. The aim of this study was to establish a predictive model of mortality during hospitalization by extracting clinical data from the Medical Information Mart for Intensive Care III (MIMIC-III) database. MethodsThe clinical data of patients with sepsis complicated by anemia in the MIMIC-III database were retrospectively analyzed. Indexes were screened by stepwise logistic regression (LR), and machine learning predictive models such as Decision Tree (DT), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost) were developed and compared, identifying advantages and disadvantages of each model. ResultsA total of 13,547 patients with sepsis complicated by anemia were included in the study, among which 1,827 died during hospitalization and 11,720 were still alive at discharge. The preliminary stepwise regression model selected 20 clinical indexes, including Elixhauser comorbidity index, maximum blood urea nitrogen (BUN), and maximum hemoglobin reduction. The predictive models showed good discriminative ability (area under the receiver operating characteristic curve [AUROC]:LR, 0.777; DT, 0.726; RF, 0.788; XGBoost, 0.815) and goodness of fit (area under the precision-recall curve [AUPRC]: LR, 0.350; DT, 0.290; RF, 0.400; XGBoost, 0.428). The Shapley Additive exPlanation (SHAP) values in the XGBoost model showed that Elixhauser comorbidity index, maximum BUN, maximum hemoglobin reduction, ventilator use within 24 hours of admission, and age were significant features for predicting in-hospital mortality in patients with sepsis complicated by anemia. ConclusionsThe XGBoost model had better discrimination ability and goodness of fit when compared with other models. Machine learning algorithms have significant practical value in the development of an early warning system for patients with sepsis complicated by anemia.

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“…Based on imaging, ensemble ML models, which group the prediction of different weak learners, have demonstrated higher accuracy than expert readers for the diagnosis of obstructive coronary artery disease ( 17 ); a DL model automated the diagnosis of acute ischemic infarction using CT studies ( 18 ); and another DL model achieved 92.3% accuracy for left ventricular hypertrophy classification analysing echocardiographic images ( 19 ). Different ML models have also operated on electronic health records (EHR) for triaging of low-risk vs. high-risk cardiovascular patients, grading findings as requiring non-urgent, urgent or critical attention, as a strategy to improve efficiency and allocation of the finite resources available in the emergency department ( 20 ). Lastly, a ML ensemble model combined clinical data, quantitative stenosis, and plaque metrics from CT angiography to effectively detect lesion-specific ischemia ( 21 ).…”

Section: Status Interpretation—comparison To Populationmentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.

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Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease

Cited by 43 publications

References 35 publications

Prediction of lung metastases in thyroid cancer using machine learning based on SEER database

Prediction of lung metastases in thyroid cancer using machine learning based on SEER database

A machine learning predictive model of in-hospital mortality in patients with sepsis complicated by anemia: a retrospective study based on the MIMIC-III database

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

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