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Göğebakan K, Ulu R, Abiyev R, Şah M. A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules. Health Inf Sci Syst 2024; 12:27. [PMID: 38524804 PMCID: PMC10960787 DOI: 10.1007/s13755-024-00286-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 02/28/2024] [Indexed: 03/26/2024] Open
Abstract
According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.
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Affiliation(s)
- Kadime Göğebakan
- Directorate of Information Technologies, Istanbul Technical University, North Cyprus via Mersin 10, Famagusta, Turkey
| | - Ramazan Ulu
- Department of Nephrology, School of Medicine, Adiyaman University, Adiyaman, Turkey
| | - Rahib Abiyev
- Computer Engineering Department, Near East University, North Cyprus via Mersin 10, Nicosia, Turkey
| | - Melike Şah
- Computer Engineering Department, Cyprus International University, North Cyprus via Mersin 10, Nicosia, Turkey
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Yoon S, Goh H, Lee PC, Tan HC, Teh MM, Lim DST, Kwee A, Suresh C, Carmody D, Swee DS, Tan SYT, Wong AJW, Choo CHM, Wee Z, Bee YM. Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study. JMIR Hum Factors 2024; 11:e50939. [PMID: 38869934 PMCID: PMC11211700 DOI: 10.2196/50939] [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: 07/17/2023] [Revised: 11/07/2023] [Accepted: 05/05/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management. OBJECTIVE This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application. METHODS We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed. RESULTS A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice. CONCLUSIONS Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts.
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Affiliation(s)
- Sungwon Yoon
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, SingHealth, Singapore, Singapore
| | - Hendra Goh
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Phong Ching Lee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Hong Chang Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Ming Ming Teh
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Dawn Shao Ting Lim
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Ann Kwee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Chandran Suresh
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - David Carmody
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Du Soon Swee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Sarah Ying Tse Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Andy Jun-Wei Wong
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | | | - Zongwen Wee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
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Lyu X, Li S. Professional medical education approaches: mobilizing evidence for clinicians. Front Med (Lausanne) 2023; 10:1071545. [PMID: 37575990 PMCID: PMC10419302 DOI: 10.3389/fmed.2023.1071545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 07/07/2023] [Indexed: 08/15/2023] Open
Abstract
Rapidly proliferating high-quality evidence supports daily decision-making in clinical practice. Continuing professional medical education links this evidence to practicing clinicians who are strongly motivated to improve the quality of their care by using the latest information. Approaches to professional education vary, and their effects depend on specific scenarios. This narrative review summarizes the main approaches for professional medical education that facilitate the mobilization of evidence for clinicians. It includes traditional learning (passive and active dissemination of educational materials, lectures, and mass media dissemination), constructivist learning (engaging in local consensus processes and education outreach visits, interfacing with local opinion leaders, conducting patient-mediated interventions, employing audit and feedback processes, and utilizing clinical decision-supporting systems), and blended learning approaches (the integration of in-person or online passive learning with active and creative learning by the learners). An optimized selection from these approaches is challenging but critical to clinicians and healthcare systems.
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Affiliation(s)
- Xiafei Lyu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Sheyu Li
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
- Division of Guideline and Rapid Recommendation, Cochrane China Center, MAGIC China Center, Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
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Benis A, Min H, Gong Y, Biondich P, Robinson D, Law T, Nohr C, Faxvaag A, Rennert L, Hubig N, Gimbel R. Ontologies Applied in Clinical Decision Support System Rules: Systematic Review. JMIR Med Inform 2023; 11:e43053. [PMID: 36534739 PMCID: PMC9896360 DOI: 10.2196/43053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/16/2022] [Accepted: 12/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. OBJECTIVE Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. METHODS The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. RESULTS CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. CONCLUSIONS Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.
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Affiliation(s)
| | - Hua Min
- College of Public Health, George Mason University, Fairfax, VA, United States
| | - Yang Gong
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, TX, United States
| | - Paul Biondich
- Clem McDonald Biomedical Informatics Center, Regenstrief Institute, Indianapolis, IN, United States
| | | | - Timothy Law
- Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, OH, United States
| | - Christian Nohr
- Department of Planning, Aalborg University, Aalborg, Denmark
| | - Arild Faxvaag
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lior Rennert
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | - Nina Hubig
- School of Computing, Clemson University, Clemson, SC, United States
| | - Ronald Gimbel
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
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周 祎, 石 清, 陈 向, 李 舍, 沈 百. [Ontologies Applied in Clinical Decision Support Systems for Diabetes]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:208-216. [PMID: 36647669 PMCID: PMC10409036 DOI: 10.12182/20220860201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Indexed: 01/18/2023]
Abstract
A clinical decision support system (CDSS) integrated with electronic health records helps physicians at the grassroots make patient-appropriate and evidence-based treatment decisions and improves the efficiency of diagnosis and treatment. Furthermore, using ontologies to build up the medical knowledge base and patient data for CDSS enhances the automation and transparency of the reasoning process of CDSS and helps generate interpretable and accurate treatment recommendations. Herein, we reviewed the relevant ontologies in the field of diabetes treatment and the progress and challenges concerning ontology-based CDSSs. Firstly, we elaborated on the current status and challenges of diabetes treatment in China, highlighting the urgent need to improve the efficiency and quality of medical services. Then, we presented background information about ontologies and gave an overview of the framework, methodology, and features of using ontologies to construct CDSS. After that, we reviewed the ontologies and instances of ontology-based CDSS in the field of diabetes treatment in China and abroad and summarized their construction methods and features. Last but not the least, we discussed the future prospects of the field, suggesting that integrating evidence-based medicine with ontologies to build a reliable clinical recommendation system should be the current focus of CDSS development.
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Affiliation(s)
- 祎灵 周
- 四川大学华西医院 内分泌代谢科 (成都 610041)Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 清阳 石
- 四川大学华西医院 内分泌代谢科 (成都 610041)Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 向阳 陈
- 四川大学华西医院 内分泌代谢科 (成都 610041)Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学华西医院 中国循证医学中心 中国Cochrane中心 中国MAGIC中心 (成都 610041)Chinese Evidence-based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 舍予 李
- 四川大学华西医院 内分泌代谢科 (成都 610041)Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
- 四川大学华西医院 中国循证医学中心 中国Cochrane中心 中国MAGIC中心 (成都 610041)Chinese Evidence-based Medicine Center, Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 百荣 沈
- 四川大学华西医院 内分泌代谢科 (成都 610041)Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, China
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Román-Villarán E, Alvarez-Romero C, Martínez-García A, Escobar-Rodríguez GA, García-Lozano MJ, Barón-Franco B, Moreno-Gaviño L, Moreno-Conde J, Rivas-González JA, Parra-Calderón CL. A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study. JMIR Form Res 2022; 6:e27990. [PMID: 35916719 PMCID: PMC9382545 DOI: 10.2196/27990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/24/2021] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.
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Affiliation(s)
- Esther Román-Villarán
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - German Antonio Escobar-Rodríguez
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | | | - Bosco Barón-Franco
- Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain
| | | | - Jesús Moreno-Conde
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - José Antonio Rivas-González
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Carlos Luis Parra-Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
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Wang K, Muennig PA. Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine. APPLIED COMPUTING AND INFORMATICS 2022. [DOI: 10.1108/aci-11-2021-0298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
PurposeThe study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.Design/methodology/approachThis study is a narrative review of the literature.FindingsThe body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.Originality/valueWhile research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.
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Lu W, Jiang W, Zhang N, Xue F. Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1088393. [PMID: 35126894 PMCID: PMC8816552 DOI: 10.1155/2022/1088393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/29/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022]
Abstract
Nursing medical histories are copied randomly, leading to the occurrence of incorrectly altered histories, and medical errors are on the rise year by year. Moreover, erroneous falsification of nursing medical history is a challenging issue that needs to be urgently addressed in domestic nursing medical history. In this manuscript, a caregiver association method, which is based on a multiattribute decision model, is proposed which is specifically designed for the caregiver association selection problem in intelligent caregiving decision making. In this mode, through the selection and modeling of key performance indicators and the design of combined weights, the purpose of regulating the priority of attributes is achieved. At the same time, a comprehensive evaluation index is designed to realize the joint optimization of multiple attributes. Simulation results show that the proposed nursing workstation can not only tradeoff the quality of nursing staff experience and system performance but also balance the distribution of nursing work and effectively reduce the association delay and system energy consumption..
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Affiliation(s)
- Wenjing Lu
- Nursing Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an 710038, China
| | - Wei Jiang
- Nursing Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an 710038, China
| | - Na Zhang
- Nursing Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an 710038, China
| | - Feng Xue
- Nursing Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an 710038, China
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El-Rashidy N, El-Sappagh S, Islam SMR, M. El-Bakry H, Abdelrazek S. Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges. Diagnostics (Basel) 2021; 11:diagnostics11040607. [PMID: 33805471 PMCID: PMC8067150 DOI: 10.3390/diagnostics11040607] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/17/2021] [Accepted: 03/05/2021] [Indexed: 02/07/2023] Open
Abstract
Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt;
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: (S.E.-S.); (S.M.R.I.)
| | - S. M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
- Correspondence: (S.E.-S.); (S.M.R.I.)
| | - Hazem M. El-Bakry
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt; (H.M.E.-B.); (S.A.)
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt; (H.M.E.-B.); (S.A.)
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AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10031040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.
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