Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter

Shashikant, R.; Chetankumar, P.

doi:10.1016/j.aci.2019.06.002

Cited by 30 publications

(19 citation statements)

References 10 publications

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“…We observed 26 studies that fit into this category [14,. Of these 26 studies, 11 (42%) used ML models [14,18,19,23,25,27,30,32,[34][35][36] and 3 (12%) used DL algorithms [20,31,38]. We observed that 12 studies incorporated both ML and DL models to analyze and validate different parameters [21,22,24,26,28,29,33,37,[39][40][41][42].…”

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

“…Random forest (RF) [14,21,23,[28][29][30]32,[35][36][37][39][40][41] and support vector machine (SVM) [18,22,24,26,28,34,[40][41][42] were the most used ML models observed in these studies, followed by decision tree (DT) [22,29,30,[40][41][42], logistic regression (LR) [28][29][30]40], Naive Bayes [19,28,29,41] [27,28], extreme gradient boosting [27,29], LogitBoost [21], AdaBoost [29], TreeBagger [34], and sequential feature selection [24]. The most used DL-based algorithm in the studies was k-nearest neighbors (KNN) [20,22,26,29,33,…”

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

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Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

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Background Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

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Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

View full text Add to dashboard Cite

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“…If no candidate was found, then the corresponding data were discarded, as described in line 12. The lines between 14 and 18 were to take only the first candidate point if some candidate points are consecutive; for example, given four candidate points [10,20,30,50], the two points 20 and 30 were to be filtered out, resulting in two remaining candidate points [10,50].…”

Section: Data Preparationmentioning

confidence: 99%

“…Although no studies of predictive systems for post-intubation tachycardia have been conducted, reports on related events (e.g., ventricular tachycardia, cardiac arrest, and arrhythmia) are available. In most studies, the related events were predicted using heart rate variability (HRV) as the main variable and several have adopted machine learning models such as logistic regression (LR), random forest (RF), decision tree (DT), artificial neural networks (ANN) [8][9][10][11]. Some studies have investigated feature selection algorithms to enhance model performance and identify indicative features.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Post-Intubation Tachycardia Using Machine-Learning Models

et al. 2020

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Tachycardia is defined as a heart rate greater than 100 bpm for more than 1 min. Tachycardia often occurs after endotracheal intubation and can cause serious complication in patients with cardiovascular disease. The ability to predict post-intubation tachycardia would help clinicians by notifying a potential event to pre-treat. In this paper, we predict the potential post-intubation tachycardia. Given electronic medical record and vital signs collected before tracheal intubation, we predict whether post-intubation tachycardia will occur within 10 min. Of 1931 available patient datasets, 257 remained after filtering those with inappropriate data such as outliers and inappropriate annotations. Three feature sets were designed using feature selection algorithms, and two additional feature sets were defined by statistical inspection or manual examination. The five feature sets were compared with various machine learning models such as naïve Bayes classifiers, logistic regression, random forest, support vector machines, extreme gradient boosting, and artificial neural networks. Parameters of the models were optimized for each feature set. By 10-fold cross validation, we found that an logistic regression model with eight-dimensional hand-crafted features achieved an accuracy of 80.5%, recall of 85.1%, precision of 79.9%, an F1 score of 79.9%, and an area under the receiver operating characteristic curve of 0.85.

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“…But it required to develop the algorithm, that can be applied clinically to prevent that patients with the occult nodal disease are effectively treated while eliminating the cost and the morbidity of neck dissection in patients without pathologic nodal disease. A research by Shashikant and Chetankumar, (2019) implemented an ML technique and the corresponding data were gathered from research group of data science MITU Skillogies Pune, India.. At the end of end research they suggested as to enhance the accuracy by considering deep learning approach. But still, the defects and characteristics were followed by these methods.…”

Section: Search Strategy and Selection Criteriamentioning

confidence: 99%

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Radhika*,

Priya

2020

IJITEE

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Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

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Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter

Cited by 30 publications

References 10 publications

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Prediction of Post-Intubation Tachycardia Using Machine-Learning Models

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

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