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Niu Q, Li H, Liu Y, Qin Z, Zhang LB, Chen J, Lyu Z. Toward the Internet of Medical Things: Architecture, trends and challenges. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:650-678. [PMID: 38303438 DOI: 10.3934/mbe.2024028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.
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Affiliation(s)
- Qinwang Niu
- Department of Health Services and Management, Sichuan Engineering Technical College, Deyang 618000, China
| | - Haoyue Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Yu Liu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Zhibo Qin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Li-Bo Zhang
- Department of Radiology, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang 110004, China
| | - Junxin Chen
- School of Software, Dalian University of Technology, Dalian 116621, China
| | - Zhihan Lyu
- Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden
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Gao G, Vaclavik L, Jeffery AD, Koch EC, Schafer K, Cimiotti JP, Pathak N, Duva I, Martin CL, Simpson RL. Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives. Appl Clin Inform 2024; 15:26-33. [PMID: 38198827 PMCID: PMC10781573 DOI: 10.1055/s-0043-1777455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/07/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. OBJECTIVES This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. METHODS We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. RESULTS The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. CONCLUSION This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.
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Affiliation(s)
- Grace Gao
- Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
- School of Nursing, St Catherine University, St Paul, Minnesota, United States
| | - Lindsay Vaclavik
- Department of Internal Medicine, Michael E. DeBakey VA Medical Center, Baylor College of Medicine, Houston, Texas, United States
| | - Alvin D. Jeffery
- Office of Nursing Services, Tennessee Valley Healthcare System, Nashville, Tennessee, United States
- Vanderbilt University School of Nursing, Nashville, Tennessee, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Erica C. Koch
- Veteran Affairs Quality Scholars Program, Tennessee Valley VA Healthcare System, Nashville, Tennessee, United States, Clinical Instructor of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Katherine Schafer
- Veteran Affairs Quality Scholars Program, Tennessee Valley VA Healthcare System, Nashville, Tennessee, United States, Clinical Instructor of Emergency Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Jeannie P. Cimiotti
- Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
| | - Neha Pathak
- Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
| | - Ingrid Duva
- Veteran Affairs Quality Scholars Program, Joseph Maxwell Cleland Atlanta VA Medical Center, Atlanta, Georgia, United States
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
| | - Christie L. Martin
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Roy L. Simpson
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States
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Kang HYJ, Batbaatar E, Choi DW, Choi KS, Ko M, Ryu KS. Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy. JMIR Med Inform 2023; 11:e47859. [PMID: 37999942 DOI: 10.2196/47859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/02/2023] [Accepted: 10/28/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Synthetic data generation (SDG) based on generative adversarial networks (GANs) is used in health care, but research on preserving data with logical relationships with synthetic tabular data (STD) remains challenging. Filtering methods for SDG can lead to the loss of important information. OBJECTIVE This study proposed a divide-and-conquer (DC) method to generate STD based on the GAN algorithm, while preserving data with logical relationships. METHODS The proposed method was evaluated on data from the Korea Association for Lung Cancer Registry (KALC-R) and 2 benchmark data sets (breast cancer and diabetes). The DC-based SDG strategy comprises 3 steps: (1) We used 2 different partitioning methods (the class-specific criterion distinguished between survival and death groups, while the Cramer V criterion identified the highest correlation between columns in the original data); (2) the entire data set was divided into a number of subsets, which were then used as input for the conditional tabular generative adversarial network and the copula generative adversarial network to generate synthetic data; and (3) the generated synthetic data were consolidated into a single entity. For validation, we compared DC-based SDG and conditional sampling (CS)-based SDG through the performances of machine learning models. In addition, we generated imbalanced and balanced synthetic data for each of the 3 data sets and compared their performance using 4 classifiers: decision tree (DT), random forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LGBM) models. RESULTS The synthetic data of the 3 diseases (non-small cell lung cancer [NSCLC], breast cancer, and diabetes) generated by our proposed model outperformed the 4 classifiers (DT, RF, XGBoost, and LGBM). The CS- versus DC-based model performances were compared using the mean area under the curve (SD) values: 74.87 (SD 0.77) versus 63.87 (SD 2.02) for NSCLC, 73.31 (SD 1.11) versus 67.96 (SD 2.15) for breast cancer, and 61.57 (SD 0.09) versus 60.08 (SD 0.17) for diabetes (DT); 85.61 (SD 0.29) versus 79.01 (SD 1.20) for NSCLC, 78.05 (SD 1.59) versus 73.48 (SD 4.73) for breast cancer, and 59.98 (SD 0.24) versus 58.55 (SD 0.17) for diabetes (RF); 85.20 (SD 0.82) versus 76.42 (SD 0.93) for NSCLC, 77.86 (SD 2.27) versus 68.32 (SD 2.37) for breast cancer, and 60.18 (SD 0.20) versus 58.98 (SD 0.29) for diabetes (XGBoost); and 85.14 (SD 0.77) versus 77.62 (SD 1.85) for NSCLC, 78.16 (SD 1.52) versus 70.02 (SD 2.17) for breast cancer, and 61.75 (SD 0.13) versus 61.12 (SD 0.23) for diabetes (LGBM). In addition, we found that balanced synthetic data performed better. CONCLUSIONS This study is the first attempt to generate and validate STD based on a DC approach and shows improved performance using STD. The necessity for balanced SDG was also demonstrated.
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Affiliation(s)
- Ha Ye Jin Kang
- Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea
- Department of Cancer AI & Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Republic of Korea
| | - Erdenebileg Batbaatar
- National Cancer Data Center, National Cancer Control Institute, National Cancer Center, Gyeonggi-do, Republic of Korea
| | - Dong-Woo Choi
- National Cancer Data Center, National Cancer Control Institute, National Cancer Center, Gyeonggi-do, Republic of Korea
| | - Kui Son Choi
- National Cancer Data Center, National Cancer Control Institute, National Cancer Center, Gyeonggi-do, Republic of Korea
- Department of Cancer Control and Policy, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Republic of Korea
| | - Minsam Ko
- Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea
| | - Kwang Sun Ryu
- Department of Cancer AI & Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Republic of Korea
- National Cancer Data Center, National Cancer Control Institute, National Cancer Center, Gyeonggi-do, Republic of Korea
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Blockchain for Patient Safety: Use Cases, Opportunities and Open Challenges. DATA 2022. [DOI: 10.3390/data7120182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Medical errors are recognized as major threats to patient safety worldwide. Lack of streamlined communication and an inability to share and exchange data are among the contributory factors affecting patient safety. To address these challenges, blockchain can be utilized to ensure a secure, transparent and decentralized data exchange among stakeholders. In this study, we discuss six use cases that can benefit from blockchain to gain operational effectiveness and efficiency in the patient safety context. The role of stakeholders, system requirements, opportunities and challenges are discussed in each use case in detail. Connecting stakeholders and data in complex healthcare systems, blockchain has the potential to provide an accountable and collaborative milieu for the delivery of safe care. By reviewing the potential of blockchain in six use cases, we suggest that blockchain provides several benefits, such as an immutable and transparent structure and decentralized architecture, which may help transform health care and enhance patient safety. While blockchain offers remarkable opportunities, it also presents open challenges in the form of trust, privacy, scalability and governance. Future research may benefit from including additional use cases and developing smart contracts to present a more comprehensive view on potential contributions and challenges to explore the feasibility of blockchain-based solutions in the patient safety context.
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Thirumahal R, Sudha Sadasivam G, Shruti P. Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19. SN COMPUTER SCIENCE 2022; 3:428. [PMID: 35965952 PMCID: PMC9362348 DOI: 10.1007/s42979-022-01298-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/01/2022] [Indexed: 11/29/2022]
Abstract
The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.
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Affiliation(s)
- R. Thirumahal
- Department of Computer Science and Engineering, P.S.G College of Technology, Coimbatore, India
| | - G. Sudha Sadasivam
- Department of Computer Science and Engineering, P.S.G College of Technology, Coimbatore, India
| | - P. Shruti
- Department of Computer Science and Engineering, P.S.G College of Technology, Coimbatore, India
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Zamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, Azizan MM, Hasikin K. Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management. Front Public Health 2021; 9:782203. [PMID: 34869194 PMCID: PMC8637834 DOI: 10.3389/fpubh.2021.782203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/25/2021] [Indexed: 01/25/2023] Open
Abstract
The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.
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Affiliation(s)
- Aizat Hilmi Zamzam
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.,Engineering Services Department, Ministry of Health Malaysia, Putrajaya, Malaysia
| | | | - Ahmad Khairi Abdul Wahab
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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Prieto Santamaría L, Fernández Lobón D, Díaz-Honrubia AJ, Ruiz EM, Nifakos S, Rodríguez-González A. Towards the Representation of Network Assets in Health Care Environments Using Ontologies. Methods Inf Med 2021; 60:e89-e102. [PMID: 34610645 PMCID: PMC8714298 DOI: 10.1055/s-0041-1735621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objectives
The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.
Methods
Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.
Results
As a result of this work, the “Software Defined Networking Description Language—CUREX Asset Discovery Tool Ontology” (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc.
Conclusion
The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.
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Affiliation(s)
- Lucía Prieto Santamaría
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Antonio Jesús Díaz-Honrubia
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ernestina Menasalvas Ruiz
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Sokratis Nifakos
- Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
| | - Alejandro Rodríguez-González
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
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