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Bacharaki D, Petrakis I, Stylianou K. Redefying the therapeutic strategies against cardiorenal morbidity and mortality: Patient phenotypes. World J Cardiol 2023; 15:76-83. [PMID: 37033683 PMCID: PMC10074996 DOI: 10.4330/wjc.v15.i3.76] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/31/2022] [Accepted: 02/22/2023] [Indexed: 03/21/2023] Open
Abstract
Chronic kidney disease (CKD) patients face an unacceptably high morbidity and mortality, mainly from cardiovascular diseases. Diabetes mellitus, arterial hypertension and dyslipidemia are highly prevalent in CKD patients. Established therapeutic protocols for the treatment of diabetes mellitus, arterial hypertension, and dyslipidemia are not as effective in CKD patients as in the general population. The role of non-traditional risk factors (RF) has gained interest in the last decades. These entail the deranged clinical spectrum of secondary hyperparathyroidism involving vascular and valvular calcification, under the term “CKD-mineral and bone disorder” (CKD-MBD), uremia per se, inflammation and oxidative stress. Each one of these non-traditional RF have been addressed in various study designs, but the results do not exhibit any applied clinical benefit for CKD-patients. The “crusade” against cardiorenal morbidity and mortality in CKD-patients is in some instances, derailed. We propose a therapeutic paradigm advancing from isolated treatment targets, as practiced today, to precision medicine involving patient phenotypes with distinct underlying pathophysiology. In this regard we propose two steps, based on current stratification management of corona virus disease-19 and sepsis. First, select patients who are expected to have a high mortality, i.e., a prognostic enrichment. Second, select patients who are likely to respond to a specific therapy, i.e., a predictive enrichment.
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Affiliation(s)
- Dimitra Bacharaki
- Nephrology Unit, 2nd Department of Internal Medicine, Attikon University Hospital, Chaidari 12462, Greece
| | - Ioannis Petrakis
- Department of Nephrology, Heraklion University Hospital, University of Crete, Heraklion 71500, Greece
| | - Kostas Stylianou
- Department of Nephrology, Heraklion University Hospital, University of Crete, Heraklion 71500, Greece
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2
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Grobe N, Scheiber J, Zhang H, Garbe C, Wang X. Omics and Artificial Intelligence in Kidney Diseases. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:47-52. [PMID: 36723282 DOI: 10.1053/j.akdh.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/28/2022] [Accepted: 11/16/2022] [Indexed: 01/20/2023]
Abstract
Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.
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Affiliation(s)
| | | | | | - Christian Garbe
- Frankfurter Innovationszentrum Biotechnologie, Frankfurt am Main, Germany
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3
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Aguiar R, Bourmpaki E, Bunce C, Coker B, Delaney F, de Jongh L, Oliveira G, Weir A, Higgins F, Spiridou A, Hasan S, Smith J, Mulla A, Glampson B, Mercuri L, Montero R, Hernandez-Fuentes M, Roufosse CA, Simmonds N, Clatworthy M, McLean A, Ploeg R, Davies J, Várnai KA, Woods K, Lord G, Pruthi R, Breen C, Chowdhury P. Incidence, Risk Factors, and Effect on Allograft Survival of Glomerulonephritis Post-transplantation in a United Kingdom Population: Cohort Study. FRONTIERS IN NEPHROLOGY 2022; 2:923813. [PMID: 37675026 PMCID: PMC10479671 DOI: 10.3389/fneph.2022.923813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/17/2022] [Indexed: 09/08/2023]
Abstract
Background Post-transplant glomerulonephritis (PTGN) has been associated with inferior long-term allograft survival, and its incidence varies widely in the literature. Methods This is a cohort study of 7,623 patients transplanted between 2005 and 2016 at four major transplant UK centres. The diagnosis of glomerulonephritis (GN) in the allograft was extracted from histology reports aided by the use of text-mining software. The incidence of the four most common GN post-transplantation was calculated, and the risk factors for disease and allograft outcomes were analyzed. Results In total, 214 patients (2.8%) presented with PTGN. IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and membranoproliferative/mesangiocapillary GN (MPGN/MCGN) were the four most common forms of post-transplant GN. Living donation, HLA DR match, mixed race, and other ethnic minority groups were associated with an increased risk of developing a PTGN. Patients with PTGN showed a similar allograft survival to those without in the first 8 years of post-transplantation, but the results suggest that they do less well after that timepoint. IgAN was associated with the best allograft survival and FSGS with the worst allograft survival. Conclusions PTGN has an important impact on long-term allograft survival. Significant challenges can be encountered when attempting to analyze large-scale data involving unstructured or complex data points, and the use of computational analysis can assist.
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Affiliation(s)
- Rute Aguiar
- Department of Transplantation and Renal Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Elli Bourmpaki
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Catey Bunce
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Bola Coker
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Florence Delaney
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Leonardo de Jongh
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Giovani Oliveira
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Alistair Weir
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Finola Higgins
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Anastasia Spiridou
- Data Research, Innovation and Virtual Environments Unit (DRIVE), Great Ormond Street Hospital for Children National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Syed Hasan
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Jonathan Smith
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | - Abdulrahim Mulla
- National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare National Health Service (NHS) Trust, Hammersmith Hospital, London, United Kingdom
| | - Ben Glampson
- Research Informatics Team, Imperial College Healthcare National Health Service (NHS) Trust, London, United Kingdom
| | - Luca Mercuri
- National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre, Imperial College London and Imperial College Healthcare National Health Service (NHS) Trust, Hammersmith Hospital, London, United Kingdom
| | - Rosa Montero
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Guy’s & St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, London, United Kingdom
| | | | - Candice A. Roufosse
- Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Naomi Simmonds
- Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Menna Clatworthy
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Adam McLean
- Renal Section, Department of Medicine, Hammersmith Hospital Campus, Imperial College London, London, United Kingdom
| | - Rutger Ploeg
- Nuffield Department of Surgical Sciences, Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Jim Davies
- National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre, Big Data Institute, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Kinga Anna Várnai
- National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre, Big Data Institute, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, Oxfordshire, United Kingdom
| | - Kerrie Woods
- National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre, Big Data Institute, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, Oxfordshire, United Kingdom
| | - Graham Lord
- Faculty of Biology Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Rishi Pruthi
- Department of Transplantation and Renal Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Cormac Breen
- Department of Transplantation and Renal Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Paramit Chowdhury
- Department of Transplantation and Renal Medicine, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
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4
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Jiang J, Chan L, Nadkarni GN. The promise of artificial intelligence for kidney pathophysiology. Curr Opin Nephrol Hypertens 2022; 31:380-386. [PMID: 35703218 PMCID: PMC10309072 DOI: 10.1097/mnh.0000000000000808] [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] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
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Affiliation(s)
- Joy Jiang
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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5
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Abstract
A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.
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Yang C, Gao B, Zhao X, Su Z, Sun X, Wang HY, Zhang P, Wang R, Liu J, Tang W, Zhang D, Chu H, Wang J, Wang F, Wang S, Zuo L, Wang Y, Yu F, Wang H, Zhang L, Zhang H, Yang L, Chen J, Zhao MH. Executive summary for China Kidney Disease Network (CK-NET) 2016 Annual Data Report. Kidney Int 2021; 98:1419-1423. [PMID: 33276868 DOI: 10.1016/j.kint.2020.09.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/31/2020] [Accepted: 09/03/2020] [Indexed: 12/16/2022]
Abstract
Chronic kidney disease (CKD) has been recognized as a public health problem globally. The spectrum of CKD in China has been evolving toward that of developed countries, which will have enormous impacts on the health care system. However, there has been no well-established national surveillance system for kidney diseases. Furthermore, China still faces several challenges of kidney care, including limited capacity and efficiency, suboptimal awareness, and huge heterogeneity in diagnosis and treatment. The China Kidney Disease Network has published 2 reports regarding the burden of CKD and end-stage kidney disease in China and intends to become a comprehensive surveillance system for kidney diseases based on multisource data. With the expansion of research group and data sources, the content of the China Kidney Disease Network 2016 Annual Data Report was further enriched. Section I addresses the epidemiologic characteristics of patients with CKD based on a national inpatient database, Hospital Quality Monitoring System, covering more than 52% of China's tertiary hospitals in China in 2016. Section II focuses on the burden of patients receiving dialysis, mainly based on the nationwide claims database, China Health Insurance Research Association database, which collects data from approximately 2% of the insured population from the municipalities/provincial capital cities and approximately 5% from the prefecture-level cities. An independent chapter regarding dialysis in 3 provincial dialysis quality control centers has been added. The China Kidney Disease Network 2016 Annual Data Report symbolizes a successful team effort in the era of big data, with support from the specialists and partners of the collaborative network, which is of substantial value for understanding the burden of kidney diseases in China and developing prevention and control strategies.
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Affiliation(s)
- Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Bixia Gao
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinju Zhao
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Zaiming Su
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Xiaoyu Sun
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Huai-Yu Wang
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Ping Zhang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Rong Wang
- Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Jian Liu
- Division of Nephrology, the First Hospital of Xinjiang University of Medicine, Uramuqi, Xinjiang, China
| | - Wen Tang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Dongliang Zhang
- Department of Nephrology, Peking University International Hospital, Beijing, China
| | - Hong Chu
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinwei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Fang Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Song Wang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Li Zuo
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Yue Wang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Feng Yu
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Department of Nephrology, Peking University International Hospital, Beijing, China
| | - Haibo Wang
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; National Institute of Health Data Science at Peking University, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China.
| | - Hong Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Li Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Jianghua Chen
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ming-Hui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China; Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China; Peking-Tsinghua Center for Life Sciences, Beijing, China
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7
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Alsunaidi SJ, Almuhaideb AM, Ibrahim NM, Shaikh FS, Alqudaihi KS, Alhaidari FA, Khan IU, Aslam N, Alshahrani MS. Applications of Big Data Analytics to Control COVID-19 Pandemic. SENSORS (BASEL, SWITZERLAND) 2021; 21:2282. [PMID: 33805218 PMCID: PMC8037067 DOI: 10.3390/s21072282] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/20/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.
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Affiliation(s)
- Shikah J. Alsunaidi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Abdullah M. Almuhaideb
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Nehad M. Ibrahim
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fatema S. Shaikh
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Kawther S. Alqudaihi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Fahd A. Alhaidari
- Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (S.J.A.); (N.M.I.); (K.S.A.); (I.U.K.); (N.A.)
| | - Mohammed S. Alshahrani
- Department of Emergency Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
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8
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Zhang L, Zhao MH, Zuo L, Wang Y, Yu F, Zhang H, Wang H. China Kidney Disease Network (CK-NET) 2016 Annual Data Report. Kidney Int Suppl (2011) 2020; 10:e97-e185. [PMID: 33304640 DOI: 10.1016/j.kisu.2020.09.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.,National Institute of Health Data Science at Peking University, Beijing, China.,Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang, China
| | - Ming-Hui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.,Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Beijing, China
| | - Li Zuo
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Yue Wang
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Feng Yu
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.,Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.,Department of Nephrology, Peking University International Hospital, Beijing, China
| | - Hong Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.,Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Haibo Wang
- National Institute of Health Data Science at Peking University, Beijing, China
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9
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Yang C, Kong G, Wang L, Zhang L, Zhao MH. Big data in nephrology: Are we ready for the change? Nephrology (Carlton) 2019; 24:1097-1102. [PMID: 31314170 DOI: 10.1111/nep.13636] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2019] [Indexed: 01/25/2023]
Abstract
Chronic kidney disease (CKD) is a major public health issue worldwide. However, the status of kidney health care needs to be strengthened globally and research evidence in nephrology is relatively limited. The unmet needs in nephrology leave ample space for imagination regarding leveraging big data and artificial intelligence (AI). Big data has potential to drive medical innovation, reduce medical costs and improve health care quality. Compared with other specialties such as cardiology, the scopes of utilizing big data in nephrology need to be enhanced. We reviewed the studies on the application of big data in nephrology, such as disease surveillance, risk prediction and clinical decision support systems (CDSS), and proposed several potential directions of utilizing big data and AI. The efforts including building a CKD surveillance system and collaborative network, implementing a real-world cohort in a cost-effective manner, strengthening the application and transformation of AI and CDSS, and stimulating the activeness of medical imaging in nephrology, could be considered. In the era of big data, a nephrologist would be stronger and smarter if he or she could get intelligent assistance from knowledge or big data-driven CDSS.
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Affiliation(s)
- Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China
| | - Guilan Kong
- National Institute of Health Data Science at Peking University, Beijing, China
| | - Liwei Wang
- Key Laboratory of Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.,National Institute of Health Data Science at Peking University, Beijing, China
| | - Ming-Hui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Beijing, China
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10
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Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R. Big science and big data in nephrology. Kidney Int 2019; 95:1326-1337. [PMID: 30982672 DOI: 10.1016/j.kint.2018.11.048] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 11/11/2018] [Accepted: 11/20/2018] [Indexed: 12/16/2022]
Abstract
There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.
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Affiliation(s)
- Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany; Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany; Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany.
| | - Markus M Rinschen
- Department II of Internal Medicine, and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany; Center for Mass Spectrometry and Metabolomics, The Scripps Research Institute, La Jolla, California, USA
| | - Jürgen Floege
- RWTH Aachen, Department of Nephrology and Clinical Immunology, Aachen, Germany
| | - Rafael Kramann
- RWTH Aachen, Department of Nephrology and Clinical Immunology, Aachen, Germany; Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands.
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Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 2018; 114:57-65. [PMID: 29673604 DOI: 10.1016/j.ijmedinf.2018.03.013] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/23/2018] [Indexed: 01/02/2023]
Abstract
BACKGROUND The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. STUDY SELECTION Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. DATA EXTRACTION Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. RESULTS A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. CONCLUSION This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
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Affiliation(s)
| | - Anil Pandit
- Symbiosis Institute of Health Sciences, Pune, India
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Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy. KIDNEY DISEASES 2018; 4:1-9. [PMID: 29594137 DOI: 10.1159/000486394] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/14/2022]
Abstract
Background Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.
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Affiliation(s)
- Miguel Hueso
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Alfredo Vellido
- bIntelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Nuria Montero
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | | | - Rosa Ramos
- cFresenius Medical Care, Bad Homburg, Germany
| | - Manuel Angoso
- dDialysis Unit, Clínica Virgen del Consuelo, Valencia, Spain
| | - Josep Maria Cruzado
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Anders Jonsson
- eArtificial Intelligence and Machine Learning Research Group, Universitat Pompeu Fabra (UPF), Barcelona, Spain
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