The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

doi:10.5455/aim.2016.24.354.359

Cited by 22 publications

(7 citation statements)

References 15 publications

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“…ANN simulates the function and structure of biological neural network to establish non-linear mathematical models with strong fault tolerance, adaptiveness, nonlinear comprehensive reasoning ability, and the powerful ability to solve co-linearity and interactions between variables [ 47 , 48 ]. Although complex relationships often exist between output and input factors in the medical field, ANNs have been used in clinical settings to effectively solve this issue and successfully applied to large and complex sample statistics.…”

Section: Discussionmentioning

confidence: 99%

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Guo

Zhou

et al. 2018

PLoS Negl Trop Dis

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BackgroundIn order to better assist medical professionals, this study aimed to develop and compare the performance of three models—a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model—to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.Methodology/Principal findingsSchistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient’s outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.Conclusions/SignificancePredictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.

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Section: Discussionmentioning

confidence: 99%

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Guo

Zhou

et al. 2018

PLoS Negl Trop Dis

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“…The characteristics of included studies are summarized in Tables 1 and 2. Of the 20 included studies, 13 were of prognostic prediction models [11][12][13][14][15][16][17][18][19][20][21][22][23] and 7 were of diagnostic prediction models. [24][25][26][27][28][29][30] Among the 13 prognostic prediction models studies, the outcome being predicted were first VTE (8 studies), post-operative VTE (4 studies), and recurrent VTE (1 study).…”

Section: Study and Patient Characteristics Of Included Studiesmentioning

confidence: 99%

“…Essential elements of the model development, validation, and evaluation method were often omitted from the reports, hindering the appraisal of their performance, applicability, and reproducibility. In some studies, 11,17,26,27 sample sizes were limited, which may have led to overfitting of models. The time span of prediction, defined as the period between predictor assessment and outcomes, was not described in most studies, which also limits the clinical applicability.…”

Section: Risk Of Bias Assessmentmentioning

confidence: 99%

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis

Chiasakul,

Lam,

McNichol

et al. 2023

European J of Haematology

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BackgroundAccurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.AimsTo systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.MethodsA systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included “artificial intelligence” and “venous thromboembolism.” Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t‐test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).ResultsA total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI‐based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74–0.85) versus 0.61 (95% CI: 0.54–0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.ConclusionThe use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

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“…In recent years, the field of data science has been pioneered in the development of hardware and software for the application of Artificial Neural Networks (ANNs) in clinical analysis, which can be useful for the diagnosis of DVT and other diseases in general, for example, the use of ML models such as Decision Trees, Support Vector Machine (SVM), and Neural Networks [24][25][26]. Nowadays, there are alternative methods of DVT diagnosis, some of which use AI.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Contreras-Luján

García-Guerrero

López-Bonilla

et al. 2022

MCA

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Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.

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The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

Cited by 22 publications

References 15 publications

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

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