Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

2022 ◽  
pp. 80-127
Author(s):  
Viswanathan Rajagopalan ◽  
Houwei Cao
Keyword(s):  
Artificial Intelligence ◽  
Disease Management ◽  
Heart Attack ◽  
Congenital Heart ◽  
The United States ◽  
Disease Classification ◽  
Gradient Boosting ◽  
Support Vector ◽  
Gradient Boosting Machine ◽  
Infarction Heart

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

scholarly journals Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zia U. Ahmed ◽  
Kang Sun ◽  
Michael Shelly ◽  
Lina Mu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Mortality Rates ◽  
Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Gradient Boosting Machine ◽  
Explainable Artificial Intelligence ◽  
The Impact ◽  
Bronchus Cancer

AbstractMachine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners—generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

scholarly journals Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 219 ◽  
Author(s):  
Xiashuang Wang ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Classification Accuracy ◽  
Classification Scheme ◽  
Normal Subjects ◽  
Gradient Boosting ◽  
Support Vector ◽  
Grid Search ◽  
Automated Recognition ◽  
Eeg Data ◽  
Gradient Boosting Machine

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

scholarly journals Using machine learning for sequence-level automated MRI protocol selection in neuroradiology

2017 ◽  
Vol 25 (5) ◽  
pp. 568-571 ◽  
Author(s):  
Andrew D Brown ◽  
Thomas R Marotta
Keyword(s):  
Machine Learning ◽  
Clinical Findings ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Model ◽  
Imaging Protocol ◽  
Sequence Selection ◽  
Gradient Boosting Machine ◽  
Protocol Selection ◽  
Magnetic Resonance Imaging Mri

Abstract Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models – support vector machine, gradient boosting machine, and random forest – to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

scholarly journals Machine Learning Applied to Omics Datasets Predicts Mortality in Patients with Alcoholic Hepatitis

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 41
Author(s):  
Bei Gao ◽  
Tsung-Chin Wu ◽  
Sonja Lang ◽  
Lu Jiang ◽  
Yi Duan ◽  
...  
Keyword(s):  
Alcoholic Hepatitis ◽  
The United States ◽  
Meld Score ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Support Vector ◽  
Risk Of Death ◽  
Bacterial Microbiota ◽  
Health Care Burden ◽  
End Stage

Alcoholic hepatitis is a major health care burden in the United States due to significant morbidity and mortality. Early identification of patients with alcoholic hepatitis at greatest risk of death is extremely important for proper treatments and interventions to be instituted. In this study, we used gradient boosting, random forest, support vector machine and logistic regression analysis of laboratory parameters, fecal bacterial microbiota, fecal mycobiota, fecal virome, serum metabolome and serum lipidome to predict mortality in patients with alcoholic hepatitis. Gradient boosting achieved the highest AUC of 0.87 for both 30-day mortality prediction using the bacteria and metabolic pathways dataset and 90-day mortality prediction using the fungi dataset, which showed better performance than the currently used model for end-stage liver disease (MELD) score.

scholarly journals A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang

2021 ◽  
Author(s):  
Naipeng Liu ◽  
Hui Gao ◽  
Zhen Zhao ◽  
Yule Hu ◽  
Longchen Duan
Keyword(s):  
Pearson Correlation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Model ◽  
Rate Of Penetration ◽  
Gas Drilling ◽  
Light Gradient ◽  
Stacked Generalization ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting

AbstractIn gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the drilling operational parameters and reduce its overall cost. To predict ROP with satisfactory precision, a stacked generalization ensemble model is developed in this paper. Drilling data were collected from a shale gas survey well in Xinjiang, northwestern China. First, Pearson correlation analysis is used for feature selection. Then, a Savitzky-Golay smoothing filter is used to reduce noise in the dataset. In the next stage, we propose a stacked generalization ensemble model that combines six machine learning models: support vector regression (SVR), extremely randomized trees (ET), random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGB). The stacked model generates meta-data from the five models (SVR, ET, RF, GB, LightGBM) to compute ROP predictions using an XGB model. Then, the leave-one-out method is used to verify modeling performance. The performance of the stacked model is better than each single model, with R2 = 0.9568 and root mean square error = 0.4853 m/h achieved on the testing dataset. Hence, the proposed approach will be useful in optimizing gas drilling. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the relevant ROP parameters.

scholarly journals Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2780 ◽  
Author(s):  
Muhammad E. H. Chowdhury ◽  
Khawla Alzoubi ◽  
Amith Khandakar ◽  
Ridab Khallifa ◽  
Rayaan Abouhasera ◽  
...  
Keyword(s):  
Real Time ◽  
Heart Attack ◽  
The United States ◽  
Attack Detection ◽  
The Other ◽  
Support Vector ◽  
Linear Classification ◽  
Road Accidents ◽  
Time Frequency ◽  
St Elevation

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

scholarly journals Forecasting the Walking Assistance Rehabilitation Level of Stroke Patients Using Artificial Intelligence

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1096
Author(s):  
Kanghyeon Seo ◽  
Bokjin Chung ◽  
Hamsa Priya Panchaseelan ◽  
Taewoo Kim ◽  
Hyejung Park ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Medical Rehabilitation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Prescription Data ◽  
Automated Classification ◽  
Classification Models ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Tree Models

Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients’ (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over 90% accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost 92% in accuracy, recall, precision, and F1-score, and 86.8% in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.

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