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Zhan K, Buhler KA, Chen IY, Fritzler MJ, Choi MY. Systemic lupus in the era of machine learning medicine. Lupus Sci Med 2024; 11:e001140. [PMID: 38443092 PMCID: PMC11146397 DOI: 10.1136/lupus-2023-001140] [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: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.
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Affiliation(s)
- Kevin Zhan
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Katherine A Buhler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Irene Y Chen
- Computational Precision Health, University of California Berkeley and University of California San Francisco, Berkeley, California, USA
- Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, USA
| | - Marvin J Fritzler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - May Y Choi
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada
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2
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Liu F, Ye J, Wang S, Li Y, Yang Y, Xiao J, Jiang A, Lu X, Zhu Y. Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study. Mediators Inflamm 2024; 2024:3188216. [PMID: 38385005 PMCID: PMC10881253 DOI: 10.1155/2024/3188216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/02/2023] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
Background Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA. Methods We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed. Results Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes. Conclusion CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.
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Affiliation(s)
- Fucun Liu
- Department of Orthopedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Juelan Ye
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China
- Spinal Tumor Center, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Shouli Wang
- Orthopedics Research Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Zhejiang, China
| | - Yang Li
- Department of Orthopedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yuhang Yang
- Department of Orthopedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jianru Xiao
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China
- Spinal Tumor Center, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Aimin Jiang
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xuhua Lu
- Department of Orthopedics, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yunli Zhu
- Department of Orthopedics, Changzheng Hospital, Naval Medical University, Shanghai, China
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Zhou W, Chen Y, Zheng Y, Bai Y, Yin J, Wu XX, Hong M, Liang L, Zhang J, Gao Y, Sun N, Li J, Zhang Y, Wu L, Jin X, Niu J. Characterizing immune variation and diagnostic indicators of preeclampsia by single-cell RNA sequencing and machine learning. Commun Biol 2024; 7:32. [PMID: 38182876 PMCID: PMC10770323 DOI: 10.1038/s42003-023-05669-2] [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: 04/02/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Preeclampsia is a multifactorial and heterogeneous complication of pregnancy. Here, we utilize single-cell RNA sequencing to dissect the involvement of circulating immune cells in preeclampsia. Our findings reveal downregulation of immune response in lymphocyte subsets in preeclampsia, such as reduction in natural killer cells and cytotoxic genes expression, and expansion of regulatory T cells. But the activation of naïve T cell and monocyte subsets, as well as increased MHC-II-mediated pathway in antigen-presenting cells were still observed in preeclampsia. Notably, we identified key monocyte subsets in preeclampsia, with significantly increased expression of angiogenesis pathways and pro-inflammatory S100 family genes in VCAN+ monocytes and IFN+ non-classical monocytes. Furthermore, four cell-type-specific machine-learning models have been developed to identify potential diagnostic indicators of preeclampsia. Collectively, our study demonstrates transcriptomic alternations of circulating immune cells and identifies immune components that could be involved in pathophysiology of preeclampsia.
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Affiliation(s)
- Wenwen Zhou
- BGI Research, Shenzhen, 518103, China
- College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
| | - Yixuan Chen
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China
| | - Yuhui Zheng
- BGI Research, Shenzhen, 518103, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yong Bai
- BGI Research, Shenzhen, 518103, China
| | | | - Xiao-Xia Wu
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China
| | - Mei Hong
- College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, South China Agricultural University, Guangzhou, 510642, China
| | - Langchao Liang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Qingdao, 266555, China
| | - Jing Zhang
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China
| | - Ya Gao
- BGI Research, Shenzhen, 518103, China
| | - Ning Sun
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China
| | | | - Yiwei Zhang
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China
| | - Linlin Wu
- Department of Obstetrics, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China.
| | - Xin Jin
- BGI Research, Shenzhen, 518103, China.
- School of Medicine, South China University of Technology, Guangzhou, 510006, China.
- Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Jianmin Niu
- Department of Obstetrics, Shenzhen Maternity & Child Healthcare Hospital, The First School of Clinical Medicine, Southern Medical University, Shenzhen, 518028, China.
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4
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Wang DC, Tang YY, He CS, Fu L, Liu XY, Xu WD. Exploring machine learning methods for predicting systemic lupus erythematosus with herpes. Int J Rheum Dis 2023; 26:2047-2054. [PMID: 37578132 DOI: 10.1111/1756-185x.14869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/03/2023] [Accepted: 08/02/2023] [Indexed: 08/15/2023]
Abstract
OBJECTIVES To investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE). METHODS A total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups. RESULTS We selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision-making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10-fold cross-validation showed the optimization of five model parameters. CONCLUSION The random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.
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Affiliation(s)
- Da-Cheng Wang
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Yang-Yang Tang
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Cheng-Song He
- Department of Rheumatology and Immunology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Lu Fu
- Laboratory Animal Center, Southwest Medical University, Luzhou, Sichuan, China
| | - Xiao-Yan Liu
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Wang-Dong Xu
- Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China
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5
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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6
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Ha MK, Bartholomeus E, Van Os L, Dandelooy J, Leysen J, Aerts O, Siozopoulou V, De Smet E, Gielen J, Guerti K, De Maeseneer M, Herregods N, Lechkar B, Wittoek R, Geens E, Claes L, Zaqout M, Dewals W, Lemay A, Tuerlinckx D, Weynants D, Vanlede K, van Berlaer G, Raes M, Verhelst H, Boiy T, Van Damme P, Jansen AC, Meuwissen M, Sabato V, Van Camp G, Suls A, Werff ten Bosch JVD, Dehoorne J, Joos R, Laukens K, Meysman P, Ogunjimi B. Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study. Pediatr Rheumatol Online J 2022; 20:91. [PMID: 36253751 PMCID: PMC9575227 DOI: 10.1186/s12969-022-00747-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/24/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. METHODS RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. RESULTS Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. CONCLUSION Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.
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Affiliation(s)
- My Kieu Ha
- Center for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium. .,Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium. .,Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium.
| | - Esther Bartholomeus
- grid.5284.b0000 0001 0790 3681Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium ,grid.411414.50000 0004 0626 3418Center of Medical Genetics, University of Antwerp, Antwerp University Hospital, Edegem, Belgium
| | - Luc Van Os
- grid.411414.50000 0004 0626 3418Ophthalmology Department, Antwerp University Hospital, Edegem, Belgium
| | - Julie Dandelooy
- grid.411414.50000 0004 0626 3418Dermatology Department, Antwerp University Hospital, Edegem, Belgium
| | - Julie Leysen
- grid.411414.50000 0004 0626 3418Dermatology Department, Antwerp University Hospital, Edegem, Belgium ,grid.5284.b0000 0001 0790 3681Department of Translational Research in Immunology and Inflammation, University of Antwerp, Wilrijk, Belgium
| | - Olivier Aerts
- grid.411414.50000 0004 0626 3418Dermatology Department, Antwerp University Hospital, Edegem, Belgium ,grid.5284.b0000 0001 0790 3681Department of Translational Research in Immunology and Inflammation, University of Antwerp, Wilrijk, Belgium
| | - Vasiliki Siozopoulou
- grid.411414.50000 0004 0626 3418Pathology Department, Antwerp University Hospital, Edegem, Belgium
| | - Eline De Smet
- grid.411414.50000 0004 0626 3418Radiology Department, Antwerp University Hospital, Edegem, Belgium
| | - Jan Gielen
- grid.411414.50000 0004 0626 3418Radiology Department, Antwerp University Hospital, Edegem, Belgium ,grid.5284.b0000 0001 0790 3681Department of Molecular – Morphology – Microscopy, University of Antwerp, Wilrijk, Belgium
| | - Khadija Guerti
- grid.411414.50000 0004 0626 3418Clinical Biology Department, Antwerp University Hospital, Edegem, Belgium
| | | | - Nele Herregods
- grid.410566.00000 0004 0626 3303Radiology Department, Ghent University Hospital, Ghent, Belgium
| | - Bouchra Lechkar
- grid.411414.50000 0004 0626 3418Department of Immunology, Allergology, and Rheumatology, Antwerp University Hospital, Edegem, Belgium
| | - Ruth Wittoek
- grid.410566.00000 0004 0626 3303Rheumatology Department, Ghent University Hospital, Ghent, Belgium ,grid.411414.50000 0004 0626 3418Rheumatology Department, Antwerp Hospital Network, Antwerp, Belgium
| | - Elke Geens
- grid.411414.50000 0004 0626 3418Rheumatology Department, Antwerp Hospital Network, Antwerp, Belgium
| | - Laura Claes
- grid.411414.50000 0004 0626 3418Pediatric Neurology Unit, Antwerp University Hospital, Edegem, Belgium
| | - Mahmoud Zaqout
- grid.411414.50000 0004 0626 3418Pediatric Cardiology Department, Antwerp University Hospital, Edegem, Belgium ,grid.411414.50000 0004 0626 3418Pediatric Cardiology Department, Antwerp Hospital Network, Antwerp, Belgium
| | - Wendy Dewals
- grid.411414.50000 0004 0626 3418Pediatric Cardiology Department, Antwerp University Hospital, Edegem, Belgium
| | - Annelies Lemay
- Department of Pediatrics, Turnhout General Hospital, Turnhout, Belgium
| | - David Tuerlinckx
- grid.7942.80000 0001 2294 713XDepartment of Pediatrics, Catholic University of Louvain, Louvain-la-Neuve, Belgium ,grid.6520.10000 0001 2242 8479Department of Pediatrics, Namur University Hospital Center, Site Dinant, Dinant, Belgium
| | - David Weynants
- grid.6520.10000 0001 2242 8479Department of Pediatrics, Namur University Hospital Center, Site Sainte-Elisabeth, Namur, Belgium
| | - Koen Vanlede
- Department of Pediatrics, Nikolaas General Hospital, Sint-Niklaas, Belgium
| | - Gerlant van Berlaer
- Department of Emergency Medicine/Pediatric Care, Brussels University Hospital, Jette, Belgium
| | - Marc Raes
- grid.414977.80000 0004 0578 1096Department of Pediatrics, Jessa Hospital, Hasselt, Belgium
| | - Helene Verhelst
- grid.410566.00000 0004 0626 3303Department of Pediatric Neurology, Ghent University Hospital, Ghent, Belgium
| | - Tine Boiy
- grid.411414.50000 0004 0626 3418Department of Pediatric Rheumatology, Antwerp University Hospital, Edegem, Belgium
| | - Pierre Van Damme
- grid.5284.b0000 0001 0790 3681Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681Center for the Evaluation of Vaccine, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
| | - Anna C. Jansen
- grid.411414.50000 0004 0626 3418Pediatric Neurology Unit, Antwerp University Hospital, Edegem, Belgium
| | - Marije Meuwissen
- grid.5284.b0000 0001 0790 3681Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
| | - Vito Sabato
- grid.411414.50000 0004 0626 3418Department of Immunology, Allergology, and Rheumatology, Antwerp University Hospital, Edegem, Belgium ,Antwerp Center for Pediatric Rheumatology and Autoinflammatory Diseases, Antwerp, Belgium
| | - Guy Van Camp
- grid.411414.50000 0004 0626 3418Center of Medical Genetics, University of Antwerp, Antwerp University Hospital, Edegem, Belgium
| | - Arvid Suls
- grid.5284.b0000 0001 0790 3681Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
| | | | - Joke Dehoorne
- grid.410566.00000 0004 0626 3303Department of Pediatric Rheumatology, Ghent University Hospital, Ghent, Belgium
| | - Rik Joos
- grid.411414.50000 0004 0626 3418Rheumatology Department, Antwerp Hospital Network, Antwerp, Belgium ,grid.411414.50000 0004 0626 3418Department of Pediatric Rheumatology, Antwerp University Hospital, Edegem, Belgium ,Antwerp Center for Pediatric Rheumatology and Autoinflammatory Diseases, Antwerp, Belgium ,grid.410566.00000 0004 0626 3303Department of Pediatric Rheumatology, Ghent University Hospital, Ghent, Belgium
| | - Kris Laukens
- grid.5284.b0000 0001 0790 3681Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681ADREM Data Lab, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681Biomedical Informatics Research Network Antwerp, University of Antwerp, Antwerp, Belgium
| | - Pieter Meysman
- grid.5284.b0000 0001 0790 3681Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681ADREM Data Lab, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681Biomedical Informatics Research Network Antwerp, University of Antwerp, Antwerp, Belgium
| | - Benson Ogunjimi
- Center for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium. .,Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium. .,Rheumatology Department, Antwerp Hospital Network, Antwerp, Belgium. .,Department of Pediatric Rheumatology, Antwerp University Hospital, Edegem, Belgium. .,Antwerp Center for Pediatric Rheumatology and Autoinflammatory Diseases, Antwerp, Belgium. .,Department of Pediatric Rheumatology, Brussels University Hospital, Jette, Belgium.
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7
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Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus. Curr Opin Rheumatol 2022; 34:374-381. [PMID: 36001343 DOI: 10.1097/bor.0000000000000902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. RECENT FINDINGS The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists. SUMMARY Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.
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