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A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective-Results of a Qualitative Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18189879. [PMID: 34574802 PMCID: PMC8465522 DOI: 10.3390/ijerph18189879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 12/26/2022]
Abstract
Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.
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Voit EO. Networks and Dynamic Models in Systems Medicine: Overview. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11661-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Ko K, Lee CW, Nam S, Ahn SV, Bae JH, Ban CY, Yoo J, Park J, Han HW. Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis. J Med Internet Res 2020; 22:e15196. [PMID: 32271154 PMCID: PMC7180516 DOI: 10.2196/15196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/08/2019] [Accepted: 01/24/2020] [Indexed: 11/25/2022] Open
Abstract
Background In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations. Objective This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors. Methods We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties. Results Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories. Conclusions We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.
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Affiliation(s)
- Kyungmin Ko
- Department of Biomedical Informatics, CHA University of Medicine, Seongnam, Republic of Korea.,Department of Pathology, Medstar Georgetown University Hospital, Washington, DC, WA, United States
| | - Chae Won Lee
- Department of Biomedical Informatics, CHA University of Medicine, Seongnam, Republic of Korea.,Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Sangmin Nam
- Department of Ophthalmology, CHA Bundang Medical Center, Seongnam, Republic of Korea
| | - Song Vogue Ahn
- Department of Health Convergence, Ewha Womans University, Seoul, Republic of Korea
| | - Jung Ho Bae
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
| | - Chi Yong Ban
- Department of Biomedical Informatics, CHA University of Medicine, Seongnam, Republic of Korea.,Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Jongman Yoo
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, Republic of Korea.,Department of Microbiology, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Jungmin Park
- Department of Nursing, School of Nursing, Hanyang University, Seoul, Republic of Korea
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University of Medicine, Seongnam, Republic of Korea.,Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, Republic of Korea
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Systems and Precision Medicine in Necrotizing Soft Tissue Infections. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1294:187-207. [PMID: 33079370 DOI: 10.1007/978-3-030-57616-5_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.
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Karakitsou E, Foguet C, de Atauri P, Kultima K, Khoonsari PE, Martins dos Santos VA, Saccenti E, Rosato A, Cascante M. Metabolomics in systems medicine: an overview of methods and applications. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Berlin R, Gruen R, Best J. Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions. Front Bioeng Biotechnol 2018; 6:112. [PMID: 30131956 PMCID: PMC6090066 DOI: 10.3389/fbioe.2018.00112] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/18/2018] [Indexed: 12/26/2022] Open
Abstract
In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).
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Affiliation(s)
- Richard Berlin
- Department of Computer Science, University of Illinois, Urbana, IL, United States
| | - Russell Gruen
- Department of Surgery, Nanyang Institute of Technology in Health and Medicine, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - James Best
- Lee Kong China School of Medicine, Nanyang Technological University, Singapore, Singapore
- Imperial College, London, United Kingdom
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Goldman AW, Burmeister Y, Cesnulevicius K, Herbert M, Kane M, Lescheid D, McCaffrey T, Schultz M, Seilheimer B, Smit A, St Laurent G, Berman B. Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity? Front Physiol 2015; 6:225. [PMID: 26347656 PMCID: PMC4541032 DOI: 10.3389/fphys.2015.00225] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 07/27/2015] [Indexed: 12/25/2022] Open
Abstract
Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.
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Affiliation(s)
- Alyssa W Goldman
- Concept Systems, Inc. Ithaca, NY, USA ; Department of Sociology, Cornell University Ithaca, NY, USA
| | | | | | - Martha Herbert
- Transcend Research Laboratory, Massachusetts General Hospital Boston, MA, USA
| | - Mary Kane
- Concept Systems, Inc. Ithaca, NY, USA
| | - David Lescheid
- International Academy of Bioregulatory Medicine Baden-Baden, Germany
| | - Timothy McCaffrey
- Division of Genomic Medicine, George Washington University Medical Center Washington, DC, USA
| | - Myron Schultz
- Biologische Heilmittel Heel GmbH Baden-Baden, Germany
| | | | - Alta Smit
- Biologische Heilmittel Heel GmbH Baden-Baden, Germany
| | | | - Brian Berman
- Center for Integrative Medicine, University of Maryland School of Medicine Baltimore, MD, USA
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Systems Medicine: The Application of Systems Biology Approaches for Modern Medical Research and Drug Development. Mol Biol Int 2015; 2015:698169. [PMID: 26357572 PMCID: PMC4556074 DOI: 10.1155/2015/698169] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Revised: 07/27/2015] [Accepted: 07/29/2015] [Indexed: 12/21/2022] Open
Abstract
The exponential development of highly advanced scientific and medical research technologies throughout the past 30 years has arrived to the point where the high number of characterized molecular agents related to pathogenesis cannot be readily integrated or processed by conventional analytical approaches. Indeed, the realization that several moieties are signatures of disease has partly led to the increment of complex diseases being characterized. Scientists and clinicians can now investigate and analyse any individual dysregulations occurring within the genomic, transcriptomic, miRnomic, proteomic, and metabolomic levels thanks to currently available advanced technologies. However, there are drawbacks within this scientific brave new age in that only isolated molecular levels are individually investigated for their influence in affecting any particular health condition. Since their conception in 1992, systems biology/medicine focuses mainly on the perturbations of overall pathway kinetics for the consequent onset and/or deterioration of the investigated condition/s. Systems medicine approaches can therefore be employed for shedding light in multiple research scenarios, ultimately leading to the practical result of uncovering novel dynamic interaction networks that are critical for influencing the course of medical conditions. Consequently, systems medicine also serves to identify clinically important molecular targets for diagnostic and therapeutic measures against such a condition.
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