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Houben F, den Heijer CD, Dukers-Muijrers NH, Smeets-Peels C, Hoebe CJ. Perceived barriers and facilitators to infection prevention and control in Dutch residential care facilities for people with intellectual and developmental disabilities: a cross-sectional study. BMC Public Health 2024; 24:704. [PMID: 38443810 PMCID: PMC10916042 DOI: 10.1186/s12889-024-18159-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Adequate implementation of infection prevention and control (IPC) in residential care facilities (RCFs) for people with intellectual and developmental disabilities (IDDs) is crucial to safeguarding this vulnerable population. Studies in this field are scarce. This study aimed to identify perceived barriers to and facilitators of IPC among professionals working in these settings, along with recommendations to improve IPC, to inform the development of targeted interventions. METHODS We administered an online questionnaire to 319 professionals from 16 Dutch RCFs for people with IDDs (March 2021-March 2022). Perceived multilevel barriers and facilitators (guideline, client, interpersonal, organisational, care sector, and policy level) were measured on a 5-point Likert scale (totally disagree-totally agree). Recommendations were assessed using a 5-point Likert scale (not at all helpful-extremely helpful), supplemented by an open-ended question. Barriers, facilitators, and recommendations were analysed by descriptive statistics. Open answers to recommendations were analysed through thematic coding. RESULTS Barriers to IPC implementation included the client group (e.g., lack of hygiene awareness) (63%), competing values between IPC and the home-like environment (42%), high work pressure (39%), and the overwhelming quantity of IPC guidelines/protocols (33%). Facilitators included perceived social support on IPC between professionals and from supervisors (90% and 80%, respectively), procedural clarity of IPC guidelines/protocols (83%), and the sense of urgency for IPC in the organisation (74%). Main recommendations included the implementation of clear IPC policies and regulations (86%), the development of a practical IPC guideline (84%), and the introduction of structural IPC education and training programmes (for new staff members) (85%). Professionals also emphasised the need for IPC improvement efforts to be tailored to the local care context, and to involve clients and their relatives. CONCLUSIONS To improve IPC in disability care settings, multifaceted strategies should be adopted. Initial efforts should involve clients (and relatives), develop a practical and context-specific IPC guideline, encourage social support among colleagues through interprofessional coaching, reduce workload, and foster an IPC culture including shared responsibility within the organisation.
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Affiliation(s)
- Famke Houben
- Department of Social Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD, Maastricht, P.O. box 616, The Netherlands.
- Department of Sexual Health, Infectious Diseases and Environmental Health, Living Lab Public Health Mosa, South Limburg Public Health Service, 6400 AA, Heerlen, P.O. box 33, The Netherlands.
| | - Casper Dj den Heijer
- Department of Social Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD, Maastricht, P.O. box 616, The Netherlands
- Department of Sexual Health, Infectious Diseases and Environmental Health, Living Lab Public Health Mosa, South Limburg Public Health Service, 6400 AA, Heerlen, P.O. box 33, The Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), 6202 AZ, Maastricht, P.O. box 5800, The Netherlands
| | - Nicole Htm Dukers-Muijrers
- Department of Sexual Health, Infectious Diseases and Environmental Health, Living Lab Public Health Mosa, South Limburg Public Health Service, 6400 AA, Heerlen, P.O. box 33, The Netherlands
- Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD, Maastricht, P.O. box 616, The Netherlands
| | | | - Christian Jpa Hoebe
- Department of Social Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, 6200 MD, Maastricht, P.O. box 616, The Netherlands
- Department of Sexual Health, Infectious Diseases and Environmental Health, Living Lab Public Health Mosa, South Limburg Public Health Service, 6400 AA, Heerlen, P.O. box 33, The Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre (MUMC+), 6202 AZ, Maastricht, P.O. box 5800, The Netherlands
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Li XH, Liao JP, Chen MK, Gao K, Wang YB, Yan SY, Huang Q, Wang YY, Shi YX, Hu WB, Jin YH. The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review. J Med Syst 2023; 48:6. [PMID: 38148352 DOI: 10.1007/s10916-023-02007-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 10/13/2023] [Indexed: 12/28/2023]
Abstract
Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.
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Affiliation(s)
- Xu-Hui Li
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Jian-Peng Liao
- School of Public Health, Wuhan University, Wuhan, 430071, China
| | - Mu-Kun Chen
- School of Computer Science, Wuhan University, Wuhan, 430071, China
| | - Kuang Gao
- School of Computer Science, Wuhan University, Wuhan, 430071, China
| | - Yong-Bo Wang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Si-Yu Yan
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Qiao Huang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yun-Yun Wang
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yue-Xian Shi
- School of Nursing, Peking University, Beijing, 100191, China
| | - Wen-Bin Hu
- School of Computer Science, Wuhan University, Wuhan, 430071, China.
| | - Ying-Hui Jin
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
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Gholamzadeh M, Abtahi H, Safdari R. The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:8550905. [PMID: 37284487 PMCID: PMC10241579 DOI: 10.1155/2023/8550905] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/07/2023] [Accepted: 02/19/2023] [Indexed: 06/08/2023]
Abstract
Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.
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Affiliation(s)
- Marsa Gholamzadeh
- Medical Informatics, Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Abtahi
- Pulmonary and Critical Care Department, Thoracic Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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Zhang Y, Razbek J, Li D, Yang L, Bao L, Xia W, Mao H, Daken M, Zhang X, Cao M. Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models. BMC Public Health 2022; 22:251. [PMID: 35135534 PMCID: PMC8822755 DOI: 10.1186/s12889-022-12617-y] [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: 06/08/2021] [Accepted: 01/17/2022] [Indexed: 12/03/2022] Open
Abstract
Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.
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Affiliation(s)
- Yan Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jaina Razbek
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Deyang Li
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lei Yang
- Xinjiang De Kang Ci Hui Health Services Group, Urumqi, Xinjiang, China
| | - Liangliang Bao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wenjun Xia
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hongkai Mao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mayisha Daken
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoxu Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mingqin Cao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
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Ali SI, Jung SW, Bilal HSM, Lee SH, Hussain J, Afzal M, Hussain M, Ali T, Chung T, Lee S. Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:226. [PMID: 35010486 PMCID: PMC8750681 DOI: 10.3390/ijerph19010226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 11/30/2022]
Abstract
Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease-mineral and bone disorder (CKD-MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD-MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach's alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect.
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Affiliation(s)
- Syed Imran Ali
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea; (S.I.A.); (H.S.M.B.)
| | - Su Woong Jung
- Department of Internal Medicine, Division of Nephrology, Kyung Hee University Hospital at Gangdong, Seoul 05278, Korea;
| | - Hafiz Syed Muhammad Bilal
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea; (S.I.A.); (H.S.M.B.)
- Department of Computing, SEECS, NUST University, Islamabad 44000, Pakistan
| | - Sang-Ho Lee
- Department of Internal Medicine, Division of Nephrology, Kyung Hee University Hospital at Gangdong, Seoul 05278, Korea;
| | - Jamil Hussain
- Department of Data Science, Sejong University, Seoul 30019, Korea;
| | - Muhammad Afzal
- Department of Software, Sejong University, Seoul 30019, Korea; (M.A.); (M.H.)
| | - Maqbool Hussain
- Department of Software, Sejong University, Seoul 30019, Korea; (M.A.); (M.H.)
| | - Taqdir Ali
- BC Children’s Hospital, University of British Columbia, Vancouver, BC V6H 3N1, Canada;
| | - Taechoong Chung
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea; (S.I.A.); (H.S.M.B.)
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea; (S.I.A.); (H.S.M.B.)
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Graham KD, Steel A, Wardle J. Embracing the Complexity of Primary Health Care: System-Based Tools and Strategies for Researching the Case Management Process. J Multidiscip Healthc 2021; 14:2817-2826. [PMID: 34934325 PMCID: PMC8678537 DOI: 10.2147/jmdh.s327260] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/31/2021] [Indexed: 12/02/2022] Open
Abstract
The provision of health care is frequently a complex process, and favourable clinical outcomes are dependent on the effective management of this complexity. Contemporary medicine and health care practices that are biomedically aligned have been informed by a reductionist paradigm, potentially creating a misalignment between health care and the human organism as a complex adaptive system. Complexity science is increasingly gaining momentum within the academic literature and is being employed across a wide range of scientific disciplines, although this is less evident in medicine. Limited evidence was found within the literature of a complexity science framework being used to explore and inform individual health care practices; in this paper, this gap will be explored through consideration of the use of strategies and tools (specifically mind maps, computer-generated network mappings, exploratory data analysis, and computer-derived network analysis) which are congruent with a complexity science framework. This information may be useful to researchers investigating health care provision and to clinicians wishing to incorporate a complexity sensibility within their practice. ![]()
Point your SmartPhone at the code above. If you have a QR code reader, the video abstract will appear. Or use: https://youtu.be/8HBU6dBY53s
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Affiliation(s)
- Kim D Graham
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology, Sydney, NSW, 2007, Australia
| | - Amie Steel
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology, Sydney, NSW, 2007, Australia
| | - Jon Wardle
- National Centre for Naturopathic Medicine, Southern Cross University, Lismore, NSW, 2480, Australia
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Hussain M, Afzal M, Malik KM, Ali T, Ali Khan W, Irfan M, Jamshed A, Lee S. Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105701. [PMID: 32882592 DOI: 10.1016/j.cmpb.2020.105701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. METHODS This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification. RESULTS ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. CONCLUSION ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.
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Affiliation(s)
- Maqbool Hussain
- Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea; Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
| | - Muhammad Afzal
- Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea.
| | - Khalid M Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
| | - Taqdir Ali
- Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea.
| | - Wajahat Ali Khan
- College of Engineering and Technology, University of Derby, Markeaton Street, Derby DE223AW, United Kingdom.
| | - Muhammad Irfan
- Department of Radiation Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A.Johar Town, Lahore 54782, Pakistan; Department of Radiation Oncology, National Guard-Health Affairs, King Abdulaziz Medical City Riyadh, Kingdom of Saudi Arabia.
| | - Arif Jamshed
- Department of Radiation Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A.Johar Town, Lahore 54782, Pakistan.
| | - Sungyoung Lee
- Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea.
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