1
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Leung R, Wang B, Gottbrecht M, Doerr A, Marya N, Soni A, McManus DD, Lin H. Association between deep neural network-derived electrocardiographic-age and incident stroke. Front Cardiovasc Med 2024; 11:1368094. [PMID: 39006167 PMCID: PMC11239432 DOI: 10.3389/fcvm.2024.1368094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 06/13/2024] [Indexed: 07/16/2024] Open
Abstract
Background Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke. Methods In this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke. Results The study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00-1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12-1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11-1.49)]. Conclusions DNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.
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Affiliation(s)
- Robert Leung
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Matthew Gottbrecht
- Division of Cardiology, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Adam Doerr
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Neil Marya
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Gastroenterology, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - David D. McManus
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Cardiology, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
- Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States
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2
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Zuo W, Yang X. A machine learning model predicts stroke associated with blood cadmium level. Sci Rep 2024; 14:14739. [PMID: 38926494 PMCID: PMC11208606 DOI: 10.1038/s41598-024-65633-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model that links blood cadmium to the identification of stroke. Our data exploring the association between blood cadmium and stroke came from the National Health and Nutrition Examination Survey (NHANES, 2013-2014). In total, 2664 participants were eligible for this study. We divided these data into a training set (80%) and a test set (20%). To analyze the relationship between blood cadmium and stroke, a multivariate logistic regression analysis was performed. We constructed and tested five ML algorithms including K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), multilayer perceptron (MLP), and random forest (RF). The best-performing model was selected to identify stroke in US adults. Finally, the features were interpreted using the Shapley Additive exPlanations (SHAP) tool. In the total population, participants in the second, third, and fourth quartiles had an odds ratio of 1.32 (95% CI 0.55, 3.14), 1.65 (95% CI 0.71, 3.83), and 2.67 (95% CI 1.10, 6.49) for stroke compared with the lowest reference group for blood cadmium, respectively. This blood cadmium-based LR approach demonstrated the greatest performance in identifying stroke (area under the operator curve: 0.800, accuracy: 0.966). Employing interpretable methods, we found blood cadmium to be a notable contributor to the predictive model. We found that blood cadmium was positively correlated with stroke risk and that stroke risk from cadmium exposure could be effectively predicted by using ML modeling.
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Affiliation(s)
- Wenwei Zuo
- School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu Area, Shanghai, 200093, China
| | - Xuelian Yang
- School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu Area, Shanghai, 200093, China.
- Department of Neurology, Shanghai Pudong New Area Gongli Hospital, No. 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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4
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Zingaro A, Ahmad Z, Kholmovski E, Sakata K, Dede' L, Morris AK, Quarteroni A, Trayanova NA. A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data. Sci Rep 2024; 14:9515. [PMID: 38664464 PMCID: PMC11045804 DOI: 10.1038/s41598-024-59997-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like theCHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.
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Affiliation(s)
- Alberto Zingaro
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA.
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
- ELEM Biotech S.L., Pier07, Via Laietana, 26, 08003, Barcelona, Spain.
| | - Zan Ahmad
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 100 Wyman Park Dr, Baltimore, MD, 21211, USA
| | - Eugene Kholmovski
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA
- Department of Radiology, University of Utah, 30 N Mario Capecchi Dr., Salt Lake City, UT, 84112, USA
| | - Kensuke Sakata
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA
| | - Luca Dede'
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Alan K Morris
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr., Salt Lake City, UT, 84112, USA
| | - Alfio Quarteroni
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Station 8, Av. Piccard, 1015, Lausanne, Switzerland
| | - Natalia A Trayanova
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA
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Sahriar S, Akther S, Mauya J, Amin R, Mia MS, Ruhi S, Reza MS. Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms. Heliyon 2024; 10:e27411. [PMID: 38495193 PMCID: PMC10943390 DOI: 10.1016/j.heliyon.2024.e27411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose.
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Affiliation(s)
- Saad Sahriar
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sanjida Akther
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Jannatul Mauya
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Ruhul Amin
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shahajada Mia
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sabba Ruhi
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shamim Reza
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
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6
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Zhang Y, Yu M, Tong C, Zhao Y, Han J. CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction. Interdiscip Sci 2024; 16:58-72. [PMID: 37626263 DOI: 10.1007/s12539-023-00583-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/27/2023]
Abstract
Stroke is still the World's second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.
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Affiliation(s)
- Yuqi Zhang
- School of Computer Science and Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Mengbo Yu
- School of Computer Science and Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Chao Tong
- School of Computer Science and Engineering, Beihang University, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
| | - Yanqing Zhao
- Department of Interventional Radiology and Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Jintao Han
- Department of Interventional Radiology and Vascular Surgery, Peking University Third Hospital, Beijing, China
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7
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Mitu M, Hasan SMM, Uddin MP, Mamun MA, Rajinikanth V, Kadry S. A stroke prediction framework using explainable ensemble learning. Comput Methods Biomech Biomed Engin 2024:1-20. [PMID: 38384147 DOI: 10.1080/10255842.2024.2316877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024]
Abstract
The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of stroke symptoms is essential to prevent strokes and promote a healthy lifestyle. FAST tests (looking for abnormalities in the face, arms, and speech) have limitations in reliability and accuracy for diagnosing strokes. This research employs machine learning (ML) techniques to develop and assess multiple ML models to establish a robust stroke risk prediction framework. This research uses a stacking-based ensemble method to select the best three machine learning (ML) models and combine their collective intelligence. An empirical evaluation of a publicly available stroke prediction dataset demonstrates the superior performance of the proposed stacking-based ensemble model, with only one misclassification. The experimental results reveal that the proposed stacking model surpasses other state-of-the-art research, achieving accuracy, precision, F1-score of 99.99%, recall of 100%, receiver operating characteristics (ROC), Mathews correlation coefficient (MCC), and Kappa scores 1.0. Furthermore, Shapley's Additive Explanations (SHAP) are employed to analyze the predictions of the black-box machine learning (ML) models. The findings highlight that age, BMI, and glucose level are the most significant risk factors for stroke prediction. These findings contribute to the development of more efficient techniques for stroke prediction, potentially saving many lives.
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Affiliation(s)
- Mostarina Mitu
- Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
| | - S M Mahedy Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
- School of Information Technology, Deakin University, Melbourne, Australia
| | - Md Al Mamun
- Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
| | - Venkatesan Rajinikanth
- Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- MEU Research Unit, Middle East University, Amman, Jordan
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8
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Zingaro A, Ahmad Z, Kholmovski E, Sakata K, Dede’ L, Morris AK, Quarteroni A, Trayanova NA. A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575156. [PMID: 38293150 PMCID: PMC10827064 DOI: 10.1101/2024.01.11.575156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.
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Affiliation(s)
- Alberto Zingaro
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
- ELEM Biotech S.L., Pier07, Via Laietana, 26, 08003, Barcelona, Spain
| | - Zan Ahmad
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 100 Wyman Park Dr, 21211, Baltimore, MD, USA
| | - Eugene Kholmovski
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA
- Department of Radiology, University of Utah, 30 N Mario Capecchi Dr., 84112, Salt Lake City, UT, USA
| | - Kensuke Sakata
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA
| | - Luca Dede’
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
| | - Alan K. Morris
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr., 84112, Salt Lake City, UT, USA
| | - Alfio Quarteroni
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Station 8, Av. Piccard, CH-1015 Lausanne, Switzerland (Professor Emeritus)
| | - Natalia A. Trayanova
- ADVANCE, Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, 3400 N. Charles St., 21218, Baltimore, MD, USA
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Sun Y, Chen X. Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:8078. [PMID: 37836909 PMCID: PMC10575143 DOI: 10.3390/s23198078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.
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Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng 137099, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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10
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An L, Qin J, Jiang W, Luo P, Luo X, Lai Y, Jin M. Non-invasive and accurate risk evaluation of cerebrovascular disease using retinal fundus photo based on deep learning. Front Neurol 2023; 14:1257388. [PMID: 37745652 PMCID: PMC10513168 DOI: 10.3389/fneur.2023.1257388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Background Cerebrovascular disease (CeVD) is a prominent contributor to global mortality and profound disability. Extensive research has unveiled a connection between CeVD and retinal microvascular abnormalities. Nonetheless, manual analysis of fundus images remains a laborious and time-consuming task. Consequently, our objective is to develop a risk prediction model that utilizes retinal fundus photo to noninvasively and accurately assess cerebrovascular risks. Materials and methods To leverage retinal fundus photo for CeVD risk evaluation, we proposed a novel model called Efficient Attention which combines the convolutional neural network with attention mechanism. This combination aims to reinforce the salient features present in fundus photos, consequently improving the accuracy and effectiveness of cerebrovascular risk assessment. Result Our proposed model demonstrates notable advancements compared to the conventional ResNet and Efficient-Net architectures. The accuracy (ACC) of our model is 0.834 ± 0.03, surpassing Efficient-Net by a margin of 3.6%. Additionally, our model exhibits an improved area under the receiver operating characteristic curve (AUC) of 0.904 ± 0.02, surpassing other methods by a margin of 2.2%. Conclusion This paper provides compelling evidence that Efficient-Attention methods can serve as effective and accurate tool for cerebrovascular risk. The results of the study strongly support the notion that retinal fundus photo holds great potential as a reliable predictor of CeVD, which offers a noninvasive, convenient and low-cost solution for large scale screening of CeVD.
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Affiliation(s)
- Lin An
- Guangdong Weiren Meditech Co., Ltd, Foshan, Guangdong, China
| | - Jia Qin
- Guangdong Weiren Meditech Co., Ltd, Foshan, Guangdong, China
| | - Weili Jiang
- Foshan Weizhi Meditech Co., Ltd, Foshan, Guangdong, China
| | - Penghao Luo
- Foshan Weizhi Meditech Co., Ltd, Foshan, Guangdong, China
| | - Xiaoyan Luo
- Department of Ophthalmology, Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Foshan, Guangdong, China
| | - Yuzheng Lai
- Department of Neurology, Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Foshan, Guangdong, China
| | - Mei Jin
- Department of Ophthalmology, Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Foshan, Guangdong, China
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11
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Abegaz TM, Baljoon A, Kilanko O, Sherbeny F, Ali AA. Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes. Comput Biol Med 2023; 164:107289. [PMID: 37557056 DOI: 10.1016/j.compbiomed.2023.107289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/01/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Major Adverse Cardiovascular Events (MACE) are common complications of type 2 diabetes mellitus (T2DM) that include myocardial infarction (MI), stroke, and heart failure (HF). The objective of the current study was to predict MACE among T2DM patients. METHODS Type 2 diabetes mellitus patients above 18 years old were recruited for the study from the All of Us Research Program. Eligible participants were those who took sodium-glucose cotransporter 2 inhibitors. Different Machine learning algorithms: including RandomForest (RF), XGBoost, logistic regression (LR), and weighted ensemble model (WEM) were employed. Clinical attributes, electrolytes and biomarkers were explored in predicting MACE. The feature importance was determined using mean decrease accuracy. RESULTS Overall, 9, 059 subjects were included in the analyses, of which 5197 (57.4%) were females. The XGBoost Model demonstrated a prediction accuracy of 0.80 [0.78-0.82], which is higher as compared to the RF 0.78[0.76-0.80], the LR model 0.65 [0.62-0.67], and the WEM 0.75 [0.73-0.76], respectively. The classification accuracy of the models for stroke was more than 95%, which was higher than prediction accuracy for MI (∼85%), and HF (∼80%). Phosphate, blood urea nitrogen and troponin levels were the major predictors of MACE. CONCLUSION The ML models had shown acceptable performance in predicting MACE in T2DM patients, except the LR model. Phosphate, blood urea nitrogen, and other electrolytes were important predictors of MACE, which is consistent between the individual components of MACE, such as stroke, MI, and HF. These parameters can be calibrated as prognostic parameters of MACE events in T2DM patients.
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Affiliation(s)
- Tadesse M Abegaz
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Ahmead Baljoon
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Oluwaseun Kilanko
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Fatimah Sherbeny
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Askal Ayalew Ali
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA.
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12
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Ahmed S, Irfan S, Kiran N, Masood N, Anjum N, Ramzan N. Remote Health Monitoring Systems for Elderly People: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:7095. [PMID: 37631632 PMCID: PMC10458487 DOI: 10.3390/s23167095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/03/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023]
Abstract
This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.
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Affiliation(s)
- Salman Ahmed
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Saad Irfan
- Department of Information Engineering Technology, National Skills University, Islamabad 44000, Pakistan;
| | - Nasira Kiran
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK; (N.K.); (N.R.)
| | - Nayyer Masood
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Nadeem Anjum
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK; (N.K.); (N.R.)
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Alruily M, El-Ghany SA, Mostafa AM, Ezz M, El-Aziz AAA. A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction. APPLIED SCIENCES 2023; 13:5047. [DOI: 10.3390/app13085047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.
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Affiliation(s)
- Meshrif Alruily
- Computer Science Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
| | - Sameh Abd El-Ghany
- Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
| | - Ayman Mohamed Mostafa
- Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
| | - Mohamed Ezz
- Computer Science Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
| | - A. A. Abd El-Aziz
- Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
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Clua-Espuny JL, Molto-Balado P, Lucas-Noll J, Panisello-Tafalla A, Muria-Subirats E, Clua-Queralt J, Queralt-Tomas L, Reverté-Villarroya S. Early Diagnosis of Atrial Fibrillation and Stroke Incidence in Primary Care: Translating Measurements into Actions-A Retrospective Cohort Study. Biomedicines 2023; 11:biomedicines11041116. [PMID: 37189734 DOI: 10.3390/biomedicines11041116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/08/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
(1) Background: AF-related strokes will triple by 2060, are associated with an increased risk of cognitive decline, and alone or in combination, will be one of the main health and economic burdens on the European population. The main goal of this paper is to describe the incidence of new AF associated with stroke, cognitive decline and mortality among people at high risk for AF. (2) Methods: Multicenter, observational, retrospective, community-based studies were conducted from 1 January 2015 to 31 December 2021. The setting was primary care centers. A total of 40,297 people aged ≥65 years without previous AF or stroke were stratified by AFrisk at 5 years. The main measurements were the overall incidence density/1000 person-years (CI95%) of AF and stroke, prevalence of cognitive decline, and Kaplan-Meier curve. (3) Results: In total, 46.4% women, 77.65 ± 8.46 years old on average showed anAF incidence of 9.9/103/year (CI95% 9.5-10.3), associated with a four-fold higher risk of stroke (CI95% 3.4-4.7), cognitive impairment(OR 1.34 (CI95% 1.1-1.5)), and all-cause mortality (OR 1.14 (CI95% 1.0-1.2)), but there was no significant difference in ischemic heart disease, chronic kidney disease, or peripheral arteriopathy. Unknown AF was diagnosed in 9.4% and of these patients, 21.1% were diagnosed with new stroke. (4) Conclusions: The patients at high AF risk (Q4th) already had an increased cardiovascular risk before they were diagnosed with AF.
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Affiliation(s)
- Josep-Lluis Clua-Espuny
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Est, Plaça Carrilet s/núm, 43500 Tortosa, Spain
- Research Support Unit Terres de l'Ebre, Institut Universitarid'Investigació en Atenció Primària Jordi Gol (IDIAP JGol), USR Terres de l'Ebre, 43500 Tortosa, Spain
| | - Pedro Molto-Balado
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP) Terres de l'Ebre, UUDDTortosa-Terres de l'Ebre, 43500 Tortosa, Spain
| | - Jorgina Lucas-Noll
- Health Department, Management CatSalut Terres de l'Ebre, 43500 Tortosa, Spain
| | - Anna Panisello-Tafalla
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Est, Plaça Carrilet s/núm, 43500 Tortosa, Spain
| | - Eulalia Muria-Subirats
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP) Terres de l'Ebre, EAP Amposta, C/Sebastià Juan Arbó, 139, 43870 Amposta, Spain
| | - Josep Clua-Queralt
- Research Support Unit Terres de l'Ebre, Institut Universitarid'Investigació en Atenció Primària Jordi Gol (IDIAP JGol), USR Terres de l'Ebre, 43500 Tortosa, Spain
| | - Lluïsa Queralt-Tomas
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Oest, Avda Cristobal Colon, 16, 43500 Tortosa, Spain
| | - Silvia Reverté-Villarroya
- Nursing Department, Campus Terres de l'Ebre, University Rovira i Virgili, Av Remolins, 13, 43500 Tortosa, Spain
- Advanced Nursing Research Group, Medicine and Health Sciences, University Rovira i Virgili, 43002 Tarragona, Spain
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15
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[Organization and costs of stroke care in outpatient settings: Systematic review]. Aten Primaria 2023; 55:102578. [PMID: 36773416 PMCID: PMC9941369 DOI: 10.1016/j.aprim.2023.102578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 02/11/2023] Open
Abstract
OBJECTIVE To review the bibliography on stroke costs (ICD-10 code I63) in the field of primary care. DESIGN Systematic review. DATA SOURCES PubMed/Medline, ClinicalTrials.gov, Cochrane Reviews, EconLit, and Ovid/Embase between 01/01/2012-12/31/2021 with descriptors included in Medical Subject Heading (MeSH). SELECTION OF STUDIES Those with a description of the costs of activities carried out in the out-of-hospital setting. Systematic reviews were included; prospective and retrospective observational studies; analysis of databases and total or partial costs of stroke as a disease (COI). Articles were added using the snowball method. The studies were excluded because: a) not specifically related to stroke; b) in editorial or commentary format; c) irrelevant after review of the title and abstract; and d) gray literature and non-academic studies were excluded. DATA EXTRACTION They were assigned a level of evidence according to the GRADE levels. Direct and indirect cost data were collected. RESULTS AND CONCLUSIONS Thirty studies, of which 14 (46.6%) were related to post-stroke costs and 12 (40%) to cardiovascular prevention costs. The results show that most of them are retrospective analyzes of different databases of short-term hospital care, and do not allow a detailed analysis of the costs by different segments of services. The possibilities for improvement are centered on primary and secondary prevention, selection and pre-hospital transfer, early discharge with support, and social and health care.
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16
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Trigka M, Dritsas E. Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models. SENSORS (BASEL, SWITZERLAND) 2023; 23:1193. [PMID: 36772237 PMCID: PMC9920214 DOI: 10.3390/s23031193] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%.
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17
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Dritsas E, Trigka M. Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction. SENSORS (BASEL, SWITZERLAND) 2023; 23:1161. [PMID: 36772201 PMCID: PMC9921621 DOI: 10.3390/s23031161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/15/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique's superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.
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18
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Dritsas E, Trigka M. Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2. SENSORS (BASEL, SWITZERLAND) 2022; 23:s23010040. [PMID: 36616638 PMCID: PMC9824026 DOI: 10.3390/s23010040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 06/12/2023]
Abstract
The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.
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19
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Hunter E, Kelleher JD. A review of risk concepts and models for predicting the risk of primary stroke. Front Neuroinform 2022; 16:883762. [DOI: 10.3389/fninf.2022.883762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.
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Padhee S, Johnson M, Yi H, Banerjee T, Yang Z. Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography. Bioengineering (Basel) 2022; 9:622. [PMID: 36354533 PMCID: PMC9687909 DOI: 10.3390/bioengineering9110622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 06/28/2024] Open
Abstract
Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground truth velocity field and projective images of dye flow patterns. The rough velocity field was estimated using the optical flow method (OFM) based on 53 projective images. ML training with least absolute shrinkage, selection operator and convolutional neural network was conducted with CFD velocity data as the ground truth and OFM velocity estimation as the input. The performance of each model was evaluated based on mean absolute error and mean squared error, where all models achieved or surpassed the criteria of 3 × 10-3 and 5 × 10-7 m/s, respectively, with a standard deviation less than 1 × 10-6 m/s. Finally, the interpretable regression and ML models were validated with over 613 image sets. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% on average for the v-velocity estimation in comparison with CFD. The ML framework provided an alternative pathway to support clinical diagnosis by predicting hemodynamic information with high efficiency and accuracy.
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Affiliation(s)
- Swati Padhee
- Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA
| | - Mark Johnson
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435, USA
| | - Hang Yi
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435, USA
| | - Tanvi Banerjee
- Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA
| | - Zifeng Yang
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435, USA
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Dritsas E, Trigka M. Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145365. [PMID: 35891045 PMCID: PMC9322993 DOI: 10.3390/s22145365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 06/12/2023]
Abstract
Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.
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22
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Dritsas E, Trigka M. Data-Driven Machine-Learning Methods for Diabetes Risk Prediction. SENSORS 2022; 22:s22145304. [PMID: 35890983 PMCID: PMC9318204 DOI: 10.3390/s22145304] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 01/11/2023]
Abstract
Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.
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Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution. Healthcare (Basel) 2022; 10:healthcare10071235. [PMID: 35885762 PMCID: PMC9323689 DOI: 10.3390/healthcare10071235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through “The COVID Tracking Project”, this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic’s severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.
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