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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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2
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Zhang S, Li H, Jing Q, Shen W, Luo W, Dai R. Anesthesia decision analysis using a cloud-based big data platform. Eur J Med Res 2024; 29:201. [PMID: 38528564 DOI: 10.1186/s40001-024-01764-0] [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: 11/26/2023] [Accepted: 03/01/2024] [Indexed: 03/27/2024] Open
Abstract
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
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Affiliation(s)
- Shuiting Zhang
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Hui Li
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Qiancheng Jing
- Department of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China
| | - Weiyun Shen
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Wei Luo
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Ruping Dai
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
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3
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Chao K, Sarker MNI, Ali I, Firdaus RR, Azman A, Shaed MM. Big data-driven public health policy making: Potential for the healthcare industry. Heliyon 2023; 9:e19681. [PMID: 37809720 PMCID: PMC10558940 DOI: 10.1016/j.heliyon.2023.e19681] [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: 04/28/2023] [Revised: 08/16/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023] Open
Abstract
The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.
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Affiliation(s)
- Kang Chao
- School of Economics and Management, Neijiang Normal University, Neijiang, 641199, China
| | - Md Nazirul Islam Sarker
- School of Social Sciences, Universiti Sains Malaysia, USM, Pinang, 11800, Malaysia
- Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh
| | - Isahaque Ali
- School of Social Sciences, Universiti Sains Malaysia, USM, Pinang, 11800, Malaysia
| | - R.B. Radin Firdaus
- School of Social Sciences, Universiti Sains Malaysia, USM, Pinang, 11800, Malaysia
| | - Azlinda Azman
- School of Social Sciences, Universiti Sains Malaysia, USM, Pinang, 11800, Malaysia
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Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Semin Arthritis Rheum 2023; 61:152213. [PMID: 37315379 DOI: 10.1016/j.semarthrit.2023.152213] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/16/2023]
Abstract
The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.
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Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain; Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
| | - Beatriz Merino-Barbancho
- Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain
| | | | - Benjamín Fernández-Gutiérrez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Ernestina Menasalvas-Ruiz
- Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
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5
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Zhou Y, Ping X, Guo Y, Heng BC, Wang Y, Meng Y, Jiang S, Wei Y, Lai B, Zhang X, Deng X. Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210637. [PMID: 36756993 DOI: 10.1002/adma.202210637] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/02/2023] [Indexed: 05/12/2023]
Abstract
Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. Here a "Mesenchymal stem cell Differentiation Prediction (MeD-P)" framework for biomaterial-induced cell lineage fate prediction is reported. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. It is shown that MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, it is demonstrated that MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation.
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Affiliation(s)
- Yingying Zhou
- Department of Dental Materials and Dental Medical Devices Testing Center, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Xianfeng Ping
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Yusi Guo
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Boon Chin Heng
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Yijun Wang
- Department of Dental Materials and Dental Medical Devices Testing Center, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Yanze Meng
- Department of Dental Materials and Dental Medical Devices Testing Center, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Shengjie Jiang
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Yan Wei
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Binbin Lai
- Biomedical Engineering Department, Peking University, Beijing, 100191, P. R. China
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, 100034, P. R. China
| | - Xuehui Zhang
- Department of Dental Materials and Dental Medical Devices Testing Center, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
| | - Xuliang Deng
- National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, NMPA Key Laboratory for Dental Materials, Beijing Laboratory of Biomedical Materials, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology, Beijing, 100081, P. R. China
- Biomedical Engineering Department, Peking University, Beijing, 100191, P. R. China
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High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set. PLOS DIGITAL HEALTH 2022; 1:e0000124. [PMID: 36812632 PMCID: PMC9931257 DOI: 10.1371/journal.pdig.0000124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 09/09/2022] [Indexed: 11/05/2022]
Abstract
High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75-2.44, p<0.01; NIS: OR 1.40, 95% CI 1.36-1.45, p<0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85-1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.
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7
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Ollenschläger M, Küderle A, Mehringer W, Seifer AK, Winkler J, Gaßner H, Kluge F, Eskofier BM. MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:5849. [PMID: 35957406 PMCID: PMC9371110 DOI: 10.3390/s22155849] [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: 06/21/2022] [Revised: 07/17/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.
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Affiliation(s)
- Malte Ollenschläger
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Wolfgang Mehringer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Ann-Kristin Seifer
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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8
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Abstract
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. Continual interdisciplinary developments in data science, biostatistics and epidemiology also motivated us to further discuss emerging topics in predictive and data-driven (hypothesis-less) analytics with machine learning. Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision-making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine.
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Affiliation(s)
- Sandra Eloranta
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Boman
- Division of Software and Computer Systems, School of Electrical Engineering and Computer Science, KTH, Stockholm, Sweden.,Department of Learning, Informatics, Management, and Ethics, Karolinska Institutet, Stockholm, Sweden
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9
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Konstantonis G, Singh KV, Sfikakis PP, Jamthikar AD, Kitas GD, Gupta SK, Saba L, Verrou K, Khanna NN, Ruzsa Z, Sharma AM, Laird JR, Johri AM, Kalra M, Protogerou A, Suri JS. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 2022; 42:215-239. [PMID: 35013839 DOI: 10.1007/s00296-021-05062-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/29/2021] [Indexed: 12/31/2022]
Abstract
The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.
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Affiliation(s)
- George Konstantonis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Ankush D Jamthikar
- Research Scientist, AtheroPoint™, USA, Roseville, CA, USA.,Visvesvaraya National Institute of Technology, Nagpur, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK.,Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13, UK
| | - Suneet K Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Kleio Verrou
- Department of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Manudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Athanasios Protogerou
- Cardiovascular Prevention Unit, Department of Pathophysiology, National Kapodistrian University of Athens, Athens, Greece
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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