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Journal articles on the topic 'Medical Decision Support System (MDSS)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Raghu, Babu Korrapati *. "COMPREHENSIVE RESEARCH MAP ON MEDICAL DECISION SUPPORT SYSTEMS (MDSS) – HISTORICAL EVOLUTION REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 399–401. https://doi.org/10.5281/zenodo.573546.

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Medical Decision Support System (MDSS) plays an increasingly crucial role in medical practices as it assists physicians to make clinical decisions and thus MDSS are expected to improve the overall quality of medical care. Research in the field of MDSS has suggested the there is a rising need to understand and discuss various developments in the field and how they have enhanced the medical and healthcare sector. This paper will provide a research roadmap to various developments over the years in the field of MDSS.

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Sejwal, Lakshita. "A Machine Learning-based Framework for Medical Decision Support Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 868–77. http://dx.doi.org/10.17762/turcomat.v11i1.13570.

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Due to the complexity of clinical decision-making and the necessity for patient-specific suggestions, medical decision support systems (MDSS) are becoming more significant in healthcare. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. Domain specialists, data scientists, and software engineers must work together to produce MDSS. This study provides a Machine Learning-based MDSS development approach that prioritizes stakeholder collaboration. Medical Decision Support Systems (MDSS) provide tailored recommendations base

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Chuprov, A. D., I. P. Bolodurina, A. O. Lositskiy, et al. "Progression signs of retinal disease used to increase the validity of an artificial intelligence-based medical decision support system." Modern technologies in ophtalmology, no. 5 (October 6, 2023): 88–91. http://dx.doi.org/10.25276/2312-4911-2023-5-88-91.

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Purpose is to determine the most significant diagnostic signs of retinal disease progression, which are used to increase the validity of the medical decision support system (mdSS) for describing oct images using the method of an expert survey. Material and methods. An expert analysis of the binary signs used in mdSS when describing the oct image of the macular area was carried out: each sign was assessed in terms of the development of the pathological process, the need for telemedicine consultation and (or) referral of patients to a medical organization. The expert method included a survey of

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Jung, Se Young, Hee Hwang, Keehyuck Lee, et al. "Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis." JMIR Medical Informatics 8, no. 7 (2020): e18758. http://dx.doi.org/10.2196/18758.

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Background Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into th

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Bontsevich, R. A., V. K. Kotlyarova, O. A. Bukach, A. V. Gubarev, D. G. Dubonosova, and I. G. Rostovceva. "Analysis of treatment approaches and the efficiency of application of a decision support program in polyclinic practice during the covid-pandemic period." Meditsinskiy sovet = Medical Council, no. 4 (April 13, 2023): 77–85. http://dx.doi.org/10.21518/ms2023-016.

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Introduction. The issues of rational treatment of a new coronavirus infection (NCI), compliance of medical prescriptions with current clinical recommendations have been extremely relevant since the beginning of the pandemic. Of particular importance is the problem of overprescribing antimicrobials.Aim. To analyze the prescriptions of medicines and evaluate the results of the implementation of the medical decision support system (MDSS) among general practitioners and general practitioners of the Belgorod outpatient department in the treatment of NCIMaterials and methods. Treatment regimens for

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Yarushev, Sergei A., Alexey N. Averkin, Egor N. Volkov, and Andrey N. Lukyanov. "PROSPECTS OF APPLICATION OF EXPLANATORY ARTIFICIAL INTELLIGENCE IN MEDICAL DECISION SUPPORT SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORKS." SOFT MEASUREMENTS AND COMPUTING 9, no. 70 (2023): 56–67. http://dx.doi.org/10.36871/2618-9976.2023.09.006.

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This paper explores the possibilities and prospects of applying explanatory artificial intelligence in medical decision support systems (MDSS) based on the use of artificial neural networks. A review of literature and sources is given. Conclusions are drawn about the benefits of using explanatory artificial intelligence in HISD by bridging the gap between system developers and endusers – clinicians.

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Rodríguez-González, Alejandro, Javier Torres-Niño, Miguel A. Mayer, Giner Alor-Hernandez, and Mark D. Wilkinson. "Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/367345.

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Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlati

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Yarushev, Sergei A., Alexey N. Averkin, and Aleksandr O. Anurov. "DEVELOPMENT OF A MODULAR SOLUTION FOR SERVICE APPLICATIONS IN PERSONALIZED MEDICINE AND HEALTHCARE 5.0." SOFT MEASUREMENTS AND COMPUTING 6, no. 79 (2024): 68–78. http://dx.doi.org/10.36871/2618-9976.2024.06.007.

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The work explores the possibilities and prospects for the use of explanatory artificial intelligence in medical decision support systems (MDSS), based on the use of artificial neural networks. The study provides a review of literature and sources. Conclusions have been drawn about the benefits of using explanatory artificial intelligence in DSS by bridging the gap between system developers and end users – clinicians.

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Madadipouya, Kasra. "A Survey on Data Mining Algorithms and Techniques in Medicine." JOIV : International Journal on Informatics Visualization 1, no. 3 (2017): 61. http://dx.doi.org/10.30630/joiv.1.3.25.

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Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the po

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Gavrilov, D. V., A. V. Kirilkina, and L. M. Serova. "Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems." Vrach i informacionnye tehnologii, no. 4 (2020): 51–58. http://dx.doi.org/10.37690/1811-0193-2020-4-51-58.

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The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care, when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management. The scientific analysis shows the manifestations versatility and the occurrence freque

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Ahmed, Ahmed Shihab, and Hussein Ali Salah. "A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2964–77. http://dx.doi.org/10.11591/eei.v12i5.4804.

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A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the

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Susanti, Susanti, Mateus Sakundarno Adi, and Atik Mawarni. "Pengembangan Sistem Pendukung Keputusan Untuk Mendukung Penegakkan Diagnosa TB Dots Di Rumah Sakit Aisyiyah Muntilan." Jurnal Manajemen Kesehatan Indonesia 4, no. 2 (2016): 123–28. http://dx.doi.org/10.14710/jmki.4.2.2016.123-128.

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Tuberculosis (TB) disease intervention is a national program and being a target of MDGs. Therefore, a government determined minimum service standards of a hospital that had to be implemented in all health service units and hospitals in Indonesia. A strategy of DOTS at Aisyiyah Hospital in Muntilan had been available particularly in terms of case finding. Notwithstanding, a process of patient diagnosis had not been implemented in accordance with a standard of human resource. Number of medical officers at a TB DOTS unit was not sufficient. In addition, quality of information like completeness, a

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Mahoney, William P., Ben Bernstein, Jamie Wolff, et al. "FHWA's Maintenance Decision Support System Project." Transportation Research Record: Journal of the Transportation Research Board 1911, no. 1 (2005): 133–42. http://dx.doi.org/10.1177/0361198105191100113.

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The Federal Highway Administration's Office of Transportation Operations Road Weather Management Program began a project in FY 1999 to develop a prototype winter road maintenance decision support system (MDSS). The MDSS capabilities are based on feedback received by the FHWA in 2001 from maintenance managers at a number of state departments of transportation (DOTs) as part of an initiative to capture surface transportation weather decision support requirements. The MDSS project goal is to seed the implementation of advanced decision support services provided by the private sector for state DOT

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WU, YEU, YONGHONG TIAN, and WANLEI ZHOU. "THE DEVELOPMENT OF A MOBILE DECISION SUPPORT SYSTEM." Journal of Interconnection Networks 02, no. 03 (2001): 379–90. http://dx.doi.org/10.1142/s0219265901000452.

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The emergence of mobile computing environments brings out various changes in the requirements and applications involving distributed data and has made the traditional Intelligent Decision Support System (IDSS) architectures based on the client/server model ineffective in mobile computing environments. This paper discusses the deficiencies of the current IDSS architectures based on data warehouse, on-line analysis processing (OLAP), model base (MB) and knowledge based (KB) technologies. By adopting the agent technology, the paper extends the IDSS system architecture to the Mobile Decision Suppo

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van Wijk, Y., I. Halilaj, E. van Limbergen, et al. "Decision Support Systems in Prostate Cancer Treatment: An Overview." BioMed Research International 2019 (June 6, 2019): 1–10. http://dx.doi.org/10.1155/2019/4961768.

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Background. A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. Methods. We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aid

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Chornous, Galyna, Yana Fareniuk, Vincentas Rolandas Giedraitis, Erstida Ulvidienė, and Ganna Kharlamova. "A data science-based marketing decision support system for brand management." Innovative Marketing 19, no. 2 (2023): 38–50. http://dx.doi.org/10.21511/im.19(2).2023.04.

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To improve the marketing activity and brand management and justify the most effective marketing decisions, organizations should implement different information technologies, mathematical methods and models into the marketing decision support system (MDSS). The goal of this paper is to form an architecture of an MDSS, the model base of which is developed on Data Science tools, in particular regression analysis and machine learning methods. The proposed MDSS is a multi-agent information system comprising nine intellectual agents (market environment monitoring, data processing, marketing mix mode

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Syed Ali Fathima S. J. and Shankar S. "AR Using NUI Based Physical Therapy Rehabilitation Framework with Mobile Decision Support System." Journal of Global Information Management 26, no. 4 (2018): 36–51. http://dx.doi.org/10.4018/jgim.2018100103.

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This article describes how physical therapy rehabilitation promotes functional ability of the disabled people to improve quality of life using Range of Motion exercises. The conventional rehabilitation seems to be effective; however, the efficiency of the treatment sessions is not guaranteed resulting in longer recovery period. Thus, there is a need of self-motivating and engaging training solution to support rehabilitation and enhance continuous assessment of disabled patients. The proposed framework is “AR-NUI-REHAB-MDSS,” augmented reality (AR) using natural user interface (NUI) based physi

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Buttery, Alan, and Rick Tamaschke. "Marketing Decision Support Systems and Australian Businesses: A Queensland Case Study and Implications Towards 2000." Journal of Management & Organization 3, no. 1 (1997): 51–58. http://dx.doi.org/10.1017/s183336720000599x.

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AbstractLittle is known about the extent to which the Marketing Decision Support System (MDSS) technology is currently used in Australia, or about the scope for the technology in Australia towards the year 2000. This paper reports the results of recent survey research into MDSS in Queensland by industry sector (agriculture and mining, manufacturing, construction, and services). The results suggest that there is an urgent need to boost the pace of MDSS development in all industry sectors, and that this should be given a high priority in government policy initiatives to enhance Australia's compe

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Buttery, Alan, and Rick Tamaschke. "Marketing Decision Support Systems and Australian Businesses: A Queensland Case Study and Implications Towards 2000." Journal of the Australian and New Zealand Academy of Management 3, no. 1 (1997): 51–58. http://dx.doi.org/10.5172/jmo.1997.3.1.51.

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AbstractLittle is known about the extent to which the Marketing Decision Support System (MDSS) technology is currently used in Australia, or about the scope for the technology in Australia towards the year 2000. This paper reports the results of recent survey research into MDSS in Queensland by industry sector (agriculture and mining, manufacturing, construction, and services). The results suggest that there is an urgent need to boost the pace of MDSS development in all industry sectors, and that this should be given a high priority in government policy initiatives to enhance Australia's compe

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Liu, Y., J. Zhou, L. Song, Q. Zou, J. Guo, and Y. Wang. "Efficient GIS-based model-driven method for flood risk management and its application in central China." Natural Hazards and Earth System Sciences 14, no. 2 (2014): 331–46. http://dx.doi.org/10.5194/nhess-14-331-2014.

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Abstract. In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effe

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Mahoney, William P., and William L. Myers. "Predicting Weather and Road Conditions: Integrated Decision-Support Tool for Winter Road-Maintenance Operations." Transportation Research Record: Journal of the Transportation Research Board 1824, no. 1 (2003): 98–105. http://dx.doi.org/10.3141/1824-11.

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Winter road-maintenance practitioners have expressed a strong interest in obtaining weather and road-condition forecasts and treatment recommendations specific to winter road-maintenance routes. These user needs led the FHWA Office of Transportation Operations Road Weather Management Program to support the development of a prototype winter road-maintenance decision-support system (MDSS). The MDSS is a unique data-fusion system designed to provide real-time treatment guidance (e.g., treatment times, types, rates, and locations) specifically regarding winter road-maintenance routes to winter mai

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Chandru, K. R., D. S. Robinson Smart, M. Ramachandran, and Chinnasami Sivaji. "Integrating the Digital Twin of Decision Support Systems in Aeronautics." 2 2, no. 2 (2023): 12–23. http://dx.doi.org/10.46632/jame/2/2/3.

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A Decision Support System (DSS) is a computerized system that helps users make decisions. In the field of air transport, RAL has developed DSSs to support decision making in various settings, including surface transportation and national security. The purpose of this chapter is to explore the maintenance hypothesis of conditional status checking and propose supporting concepts, such as enhanced care and proactive maintenance. These concepts are further enhanced by robust validation and strategies to improve the effectiveness of care in an extraordinary way. Therefore, a decision support system

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Liu, Y., J. Z. Zhou, L. X. Song, Q. Zou, J. Guo, and Y. R. Wang. "Efficient GIS-based model-driven method for flood risk management and its application in central China." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (2013): 1535–77. http://dx.doi.org/10.5194/nhessd-1-1535-2013.

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Abstract. In recent years, an important development in flood management is a focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research by the multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g. tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial approach for dynamic adaptation of support to the n

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Bolboacă, Sorana D., and Adriana Elena Bulboacă. "The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example." Studia Universitatis Babeş-Bolyai Bioethica 66, Special Issue (2021): 38. http://dx.doi.org/10.24193/subbbioethica.2021.spiss.16.

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"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is general

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Damiano, E., P. Mercogliano, N. Netti, and L. Olivares. "A "simulation chain" to define a Multidisciplinary Decision Support System for landslide risk management in pyroclastic soils." Natural Hazards and Earth System Sciences 12, no. 4 (2012): 989–1008. http://dx.doi.org/10.5194/nhess-12-989-2012.

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Abstract. This paper proposes a Multidisciplinary Decision Support System (MDSS) as an approach to manage rainfall-induced shallow landslides of the flow type (flowslides) in pyroclastic deposits. We stress the need to combine information from the fields of meteorology, geology, hydrology, geotechnics and economics to support the agencies engaged in land monitoring and management. The MDSS consists of a "simulation chain" to link rainfall to effects in terms of infiltration, slope stability and vulnerability. This "simulation chain" was developed at the Euro-Mediterranean Centre for Climate Ch

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Lin, Whei Min, Chia Sheng Tu, and Ting Chia Ou. "Support Vector Machine Based Voltage Relays for Voltage Disturbance Detection in Micro-Distribution Systems." Applied Mechanics and Materials 291-294 (February 2013): 2084–90. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2084.

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This study proposes combining fuzzy inference system and support vector machine based voltage relays for voltage disturbance detection in micro-distribution systems (MDSs). Moreover, the coordination characteristic curves of the trigger time versus dynamic errors are proposed for under-voltage and over-voltage protection. Modified coordination characteristic curves use a critical trigger time to isolate the faults. An support vector machine (SVM) is a multi-layer decision-making model, which detects voltage disturbances, such as voltage swell, voltage sag, voltage unbalance, and faults. Comput

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Sedoyeka, Eliamani, and Sophia Shabani Baruti. "Proposed Framework for Mobile Decision Support Systems for Higher Learning Institutions." International Journal of Handheld Computing Research 7, no. 3 (2016): 24–37. http://dx.doi.org/10.4018/ijhcr.2016070103.

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The vast amount of information brought about by the use of Information Technologies can sometimes pose a challenge to decision makers about the relevance and effectiveness of certain information. This paper proposes a Mobile Decision Support System (MDSS) suitable for Higher Learning institution. To arrive to the proposed design, researchers used traditional research techniques such a questionnaire and interview to understand the way decision makers operate in their respective organizations. Using four higher learning institutions located in Dar es Salaam as case study, data were collected, di

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Rodríguez-González, Alejandro, Ángel García-Crespo, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís, and Enrique Jiménez-Domingo. "Using Ontologies in Drug Prescription." International Journal of Knowledge-Based Organizations 1, no. 4 (2011): 1–15. http://dx.doi.org/10.4018/ijkbo.2011100101.

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Medical prescription has been touted as following an accurate approach to addressing particular health problems. However, the importance of the process might demand considering a formal knowledge-driven procedure to ensure its correctness which can be achieved through Medical Decision Support Systems (MDSS). Semantic Technologies have emerged as a potential silver bullet to become the backbone of those particular Information Systems since it provides seamless integration and an underlying logical formalism. This paper sheds light into using ontologies for drug prescription through the SemMed m

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Economou, G. P. K., K. Spiropoulos, and P. D. Goumas. "A novel medical decision support system." Computing & Control Engineering Journal 7, no. 4 (1996): 177–83. http://dx.doi.org/10.1049/cce:19960404.

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Petrauskas, Vytautas, Gyte Damuleviciene, Algirdas Dobrovolskis, et al. "XAI-based Medical Decision Support System Model." International Journal of Scientific and Research Publications (IJSRP) 10, no. 12 (2020): 598–607. http://dx.doi.org/10.29322/ijsrp.10.12.2020.p10869.

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Cherneha, Kyrylo S., Natalya O. Komleva, and Borys I. Tymchenko. "DECISION SUPPORT SYSTEM FOR AUTOMATED MEDICAL DIAGNOSTICS." ELECTRICAL AND COMPUTER SYSTEMS 23, no. 99 (2016): 65–72. http://dx.doi.org/10.15276/eltecs.23.99.2016.10.

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Mukhopadhyay, S. "A decision support system for medical diagnosis." Journal of Discrete Mathematical Sciences and Cryptography 3, no. 1-3 (2000): 179–92. http://dx.doi.org/10.1080/09720529.2000.10697906.

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Cândea, Ciprin, Gabriela Cândea, and Zamfirescu Bălă Constantin. "ArdoCare – a collaborative medical decision support system." Procedia Computer Science 162 (2019): 762–69. http://dx.doi.org/10.1016/j.procs.2019.12.048.

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Charles, Ifeanyi, and Chidiebere Ugwu. "A Hybrid-based Medical Decision Support System." International Journal of Computer Applications 134, no. 12 (2016): 12–18. http://dx.doi.org/10.5120/ijca2016908015.

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Grams, Ralph R., Dake Zhang, and Beidi Yue. "MDX—A medical diagnostic decision support system." Journal of Medical Systems 20, no. 3 (1996): 129–40. http://dx.doi.org/10.1007/bf02281991.

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Flügel, W. A., and C. Busch. "Development and implementation of an Integrated Water Resources Management System (IWRMS)." Advances in Science and Research 7, no. 1 (2011): 83–90. http://dx.doi.org/10.5194/asr-7-83-2011.

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Abstract. One of the innovative objectives in the EC project BRAHMATWINN was the development of a stakeholder oriented Integrated Water Resources Management System (IWRMS). The toolset integrates the findings of the project and presents it in a user friendly way for decision support in sustainable integrated water resources management (IWRM) in river basins. IWRMS is a framework, which integrates different types of basin information and which supports the development of IWRM options for climate change mitigation. It is based on the River Basin Information System (RBIS) data models and delivers

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Rao, G. R., and M. Turoff. "A hypermedia-based group decision support system to support collaborative medical decision-making." Decision Support Systems 30, no. 2 (2000): 187–216. http://dx.doi.org/10.1016/s0167-9236(00)00096-8.

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Koulaouzidis, Anastasios, Dimitris Iakovidis, Diana Yung, et al. "KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes." Endoscopy International Open 05, no. 06 (2017): E477—E483. http://dx.doi.org/10.1055/s-0043-105488.

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Abstract Background and aims Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. Methods Open-source software was used for the KID database. Clinicians contribu

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Dumitrache, Ioan, Ioana Mihu, and Monica C. Voinescu. "An Advanced Decision Support System for Medical Diagnosis." IFAC Proceedings Volumes 41, no. 2 (2008): 9607–12. http://dx.doi.org/10.3182/20080706-5-kr-1001.01625.

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Shilaskar, Swati, Ashok Ghatol, and Prashant Chatur. "Medical decision support system for extremely imbalanced datasets." Information Sciences 384 (April 2017): 205–19. http://dx.doi.org/10.1016/j.ins.2016.08.077.

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Pahlevanynejad, Shahrbanoo, Navid Danaei, Mehdi Kahouei, Majid Mirmohammadkhani, Elham Saffarieh, and Reza Safdari. "Development and validation of the Iranian Neonatal Prematurity Minimum Data Set (IMSPIMDS): a systematic review, focus group discussion, and Delphi technique." Journal of Pediatrics Review 10, no. 1 (2022): 2. http://dx.doi.org/10.32598/jpr.10.1.986.1.

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Background: Information systems help to collect information about patients. The minimum data set (MDS) provides the basis for decision-making. Objectives: This study was conducted to determine the comprehensive national MDS for prematurity information management system (IMSPIMDS) in Iran. Methods: This research is a cross-sectional study with three steps including systematic review, focus group discussion, and Delphi technique. A systematic review was conducted in relevant databases. Then a focus group discussion was used to classify the extracted data elements by contributing specializing in

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Lee, Jae Gu, Young Woong Ko, and Yong Hwan Byun. "Medical Image Decision Support System Using Image Sync Scheme." Advanced Science Letters 23, no. 4 (2017): 3691–94. http://dx.doi.org/10.1166/asl.2017.9026.

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Sawar, Mohammad Jamil. "Design and development of a medical decision Support System." Journal of Decision Systems 5, no. 3-4 (1996): 219–48. http://dx.doi.org/10.1080/12460125.1996.10511690.

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Krause, P., J. Fox, M. O'Neil, and A. Glowinski. "Can we formally specify a medical decision support system?" IEEE Expert 8, no. 3 (1993): 56–61. http://dx.doi.org/10.1109/64.215223.

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Moon Chae, Young, Quehn Park, Kwang Su Park, and Mi Young. "Development of medical decision support system for leukemia management." Expert Systems with Applications 15, no. 3-4 (1998): 309–15. http://dx.doi.org/10.1016/s0957-4174(98)00040-2.

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Yang, Junggi, Ungu Kang, and Youngho Lee. "Clinical decision support system in medical knowledge literature review." Information Technology and Management 17, no. 1 (2015): 5–14. http://dx.doi.org/10.1007/s10799-015-0216-6.

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Calloway, Stacy. "Implementation of a Clinical Decision Support System." Hospital Pharmacy 48, Supplement 2 (2013): S10—S14. http://dx.doi.org/10.1310/hpj4803-s10.

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M, Sudharshan. "Pneumonia Prediction and Decision Support System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47649.

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Abstract The rising prevalence of pneumonia demands automated diagnostic systems to enhance clinical efficiency and accuracy. Traditional diagnosis, reliant on manual chest X-ray and blood test analysis, is time-consuming and error-prone. This project introduces a deep learning-based system for pneumonia prediction, integrating chest X-ray images and blood test biomarkers to classify patients as healthy, viral, or bacterial pneumonia. It employs Convolutional Neural Networks (CNNs) for X-ray feature extraction, Random Forests for biomarker classification, and a heuristic-based fusion model for

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El Gannour, Oussama, Soufiane Hamida, Bouchaib Cherradi, et al. "Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique." Electronics 11, no. 1 (2021): 103. http://dx.doi.org/10.3390/electronics11010103.

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Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced

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Lu, Wenjing, Wei Jiang, Na Zhang, and Feng Xue. "Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model." Journal of Healthcare Engineering 2022 (January 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/1088393.

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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 o

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