A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease

Tian, Jing; Yang, Hong; Han, Gangfei; Liu, Yanling; He, Hangzhi; Han, Qinghua; Zhang, Yanbo

doi:10.1016/j.compbiomed.2022.106300

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…It is comprised of one or more hidden layers of neurons connected to an input layer and an output layer, where the data flows in only one direction, from input to output, without looping back. The MLP's hyperparameters, namely the number of hidden layers and hidden neurons, play a crucial role in the model's performance and must be selected with care [19]. Cross-validation techniques are commonly employed to determine the optimal values for these hyperparameters.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…The basic building block of an MLP is the artificial neuron or perceptron, which receives inputs, weights them, and applies an activation function to produce an output. The formula for a single neuron is given by: y = activation (Σw_i * x_i + b) where x_i are the inputs, w_i are the weights, b is the bias, Σ denotes the sum over all inputs, and activation is the activation function [19]. MLP can handle large datasets with a high number of input features, making it suitable for problems with high-dimensional data, such as CAD prediction, where numerous medical parameters can be used as input features.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

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Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Kutiame,

Millham,

Adekoya

et al. 2024

AIS

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Coronary Heart Disease (CHD) is reported to be one of the world’s deadliest diseases. Early prediction or detection and diagnosis of the disease can help prevent, cure, and reduce the damage it could cause. Artificial intelligent techniques such as Machine Learning and Deep Learning have proven useful for the early detection and prediction of disease. However, issues of irrelevant and redundant features in open datasets have contributed to low classification accuracy rates and high misclassification rates. This leaves a gap for continuous approaches for smart feature selection and high-performing models in the field for better results. This study investigates the impacts of cardiologist-inspired datasets and PCA dimensionality reduction techniques on the performance of CHD prediction. The compared results show that while PCA improves the CHD prediction accuracy for datasets obtained from cardiologists, there exist no statistically significant differences in the efficacy of the PCA method for CHD classification when applied to open datasets, however, MLP and LSTM offer promising results. The results further indicated that the effectiveness of expert-based feature selection techniques on CHD classification is relatively stable when compared with open-source datasets.

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Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Kutiame,

Millham,

Adekoya

et al. 2024

AIS

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“…CDSS for predicting coronary artery stenosis in patients with suspected coronary heart disease, developed in [13], is a noninvasive, accurate, and cost-effective alternative for evaluating the state of patients, suspected coronary heart disease by distinguishing the coronary artery stenosis.…”

Section: State-of-the-artmentioning

confidence: 99%

Methodology for the development and application of clinical decisions support information technologies with consideration of civil-legal grounds

Hnatchuk

Hovorushchenko²,

Sergeenko³

2023

reks

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Currently, there are no clinical decision support information technologies (CDSIT) that would consider civil-legal grounds when forming a decision for clinicians. Therefore, the design, development, and implementation of CDSIT, which considers civil-legal grounds when forming decisions, are actual problems. Methodology for the development and application of knowledge-driven, rule-based, clinical decisions support information technologies with consideration of civil-legal grounds has been developed, which provides a theoretical basis for developing clinical decisions support information technology with consideration of civil-legal grounds and partial CDSITs regarding the possibility of providing medical services of a certain type. In addition to the conclusion about the possibility or impossibility of providing certain medical services, the developed methodology ensures the presence of all essential terms (from the viewpoint of civil law regulation) in the contract for the certain medical service's provision and/or the data on potential patients for the provision of such a service, as well as minimization of the influence of the human factor when making clinical decisions. It is advisable to evaluate the CDSITs with consideration of civil-legal grounds, developed according to the proposed methodology, from the viewpoint of the correctness of the decisions generated by them, as well as from the viewpoint of their usefulness for clinics. In this paper, experiments with the methodology-based CDSIT regarding the possibility of performing a surrogate motherhood procedure with consideration of civil-legal grounds were conducted. Such experiments showed the correctness of the generated decisions at the level of 97 %. Experiments also demonstrated the usefulness of such IT for clinics from the viewpoint of eliminating adverse legal consequences, as they might arise due to violation or disregard of legal, and moral and ethical norms.

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“…This model can reduce the number of invasive interventions and improve patient prognosis by facilitating decision-making on the appropriate medical intervention. 25 …”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Bozyel

2024

Anatol J Cardiol

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Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors’ recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.

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A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease

Cited by 9 publications

References 28 publications

Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Methodology for the development and application of clinical decisions support information technologies with consideration of civil-legal grounds

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

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