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Abstract:The newer technologies such as data mining, machine learning, artificial intelligence and data analytics have revolutionized medical sector in terms of using the existing big data to predict the various patterns emerging from the datasets available inthe healthcare repositories. The predictions based on the existing datasets in the healthcare sector have rendered several benefits such as helping clinicians to make accurate and informed decisions while managing the patients’ health leading to better management of patients’ wellbeing and health-care coordination. The millions of people have been affected by the coronary artery disease (CAD). There are several machine learning including ensemble learning approach and deep neural networks-based algorithms have shown promising outcomes in improving prediction accuracy for early diagnosis of CAD. This paper analyses the deep neural network variant DRN, Rider Optimization Algorithm-Neural network (RideNN) and Deep Neural Network-Fuzzy Neural Network (DNFN) with application of ensemble learning method for improvement in the prediction accuracy of CAD. The experimental outcomes showed the proposed ensemble classifier achieved the highest accuracy compared to the other machine learning models. Keywords:Heart disease prediction, Deep Residual Network (DRN), Ensemble classifiers, coronary artery disease.
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Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting
Abstract Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.
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- Award ID(s):
- 1920920
- PAR ID:
- 10228537
- Date Published:
- Journal Name:
- Applied Clinical Informatics
- Volume:
- 12
- Issue:
- 01
- ISSN:
- 1869-0327
- Page Range / eLocation ID:
- 010 to 016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Purpose/ObjectivesWe aim to explore Current Procedural Terminology (CPT) codes for caregiving training services and their potential impacts on caregivers of people living with dementia. Description of the Project/ProgramIn response to the growing need for support for caregivers of people living with physical and mental health issues, CPT codes for caregiving training services will be activated for the calendar year 2024. These codes cover (1) family group behavior management and modification training services and (2) caregiver training for techniques to help patients maintain their quality of life. Caregivers will access such training support through the CPT codes provided by treating practitioners. The duration of training will vary by code. OutcomeImplementing CPT codes for caregiver training services highlights the vital role of caregivers in patient care. This support may improve their skills and communication with healthcare providers. However, timing and accessibility in care delivery need clarification, especially for caregivers of people living with dementia. Regular skill assessment and culturally competent care are essential. Before providing the service, provider training may also promote person-centered care, benefiting patients and their caregivers. ConclusionActivating CPT codes for caregiving training services may enhance caregivers' support and skills, including dementia care.more » « less
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Background and objective:Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Methods:Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived fromin vitroparticle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. Results:There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p< 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ= 0.77,p= 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ= -0.86,p= 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (bothp< 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p< 0.05). Conclusion:The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1055/s-0040-1721012
