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Articles de revues sur le sujet « Decision support systems »

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Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 8 juin 2024

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1

B verstam, U. « Decision Support and Decision Support Systems ». Radiation Protection Dosimetry 73, no 1 (1 septembre 1997) : 1–6. http://dx.doi.org/10.1093/oxfordjournals.rpd.a032107.

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Fedkova, Nadezhda Alexandrovna, et Sergey Sergeevich Chudakov. « DECISION SUPPORT SYSTEMS ». Vestnik obrazovatel'nogo konsortsiuma Srednerusskii universitet. Informatsionnye tekhnologii, no 2 (2021) : 23–26. http://dx.doi.org/10.52374/15269658_2021_18_2_23.

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Olsen, Christopher R., et William C. Bozeman. « Decision Support Systems ». Journal of Research on Computing in Education 20, no 3 (mars 1988) : 206–12. http://dx.doi.org/10.1080/08886504.1988.10781835.

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Fralic, Maryann F. « Decision support systems ». Nursing Administration Quarterly 14, no 1 (1989) : 1–8. http://dx.doi.org/10.1097/00006216-198901410-00003.

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O'Sullivan, Elizabethann. « Decision Support Systems ». Evaluation Review 9, no 1 (février 1985) : 84–92. http://dx.doi.org/10.1177/0193841x8500900105.

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French, Simon, et Murray Turoff. « Decision support systems ». Communications of the ACM 50, no 3 (mars 2007) : 39–40. http://dx.doi.org/10.1145/1226736.1226762.

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Wyatt, J. « Decision Support Systems ». Yearbook of Medical Informatics 02, no 01 (août 1993) : 325–27. http://dx.doi.org/10.1055/s-0038-1637983.

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Fieschi, M. « Decision-Support Systems ». Yearbook of Medical Informatics 03, no 01 (août 1994) : 315–17. http://dx.doi.org/10.1055/s-0038-1638008.

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Heathfield, Heather A. « Decision Support Systems ». Yearbook of Medical Informatics 04, no 01 (août 1995) : 455–57. http://dx.doi.org/10.1055/s-0038-1638034.

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Suermondt, H. J. « Decision-Support Systems ». Yearbook of Medical Informatics 05, no 01 (août 1996) : 367–70. http://dx.doi.org/10.1055/s-0038-1638061.

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Aliferis, C. F. « Decision Support Systems ». Yearbook of Medical Informatics 06, no 01 (août 1997) : 419–23. http://dx.doi.org/10.1055/s-0038-1639689.

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Finlay, Paul N. « Decision support systems ». Data Processing 28, no 8 (octobre 1986) : 434–37. http://dx.doi.org/10.1016/0011-684x(86)90427-2.

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Jacobs, Sheila M., et Sandra Pelfrey. « Decision Support Systems ». JONA : The Journal of Nursing Administration 25, no 2 (février 1995) : 46–51. http://dx.doi.org/10.1097/00005110-199502000-00007.

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Vahidov, Rustam, et Gregory E. Kersten. « Decision station : situating decision support systems ». Decision Support Systems 38, no 2 (novembre 2004) : 283–303. http://dx.doi.org/10.1016/s0167-9236(03)00099-x.

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Teufel, Andreas, et Harald Binder. « Clinical Decision Support Systems ». Visceral Medicine 37, no 6 (2021) : 491–98. http://dx.doi.org/10.1159/000519420.

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<b><i>Background:</i></b> By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. <b><i>Summary and Key Messages:</i></b> Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.
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McCartney, Patricia R. « Clinical Decision Support Systems ». MCN, The American Journal of Maternal/Child Nursing 32, no 1 (janvier 2007) : 58. http://dx.doi.org/10.1097/00005721-200701000-00014.

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Zusman, Edie E., et Yuriy Vinokur. « Clinical Decision Support Systems ». Neurosurgery 71, no 4 (octobre 2012) : N12—N14. http://dx.doi.org/10.1227/01.neu.0000419707.79663.e7.

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18

Ponedal, Steven. « Understanding Decision Support Systems ». Journal of Managed Care Pharmacy 8, no 2 (mars 2002) : 96–101. http://dx.doi.org/10.18553/jmcp.2002.8.2.96.

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Cassie, Claire. « Marketing decision support systems ». Industrial Management & ; Data Systems 97, no 8 (décembre 1997) : 293–96. http://dx.doi.org/10.1108/02635579710195000.

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Payne, Thomas H. « Computer Decision Support Systems ». Chest 118, no 2 (août 2000) : 47S—52S. http://dx.doi.org/10.1378/chest.118.2_suppl.47s.

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Fazlollahi, Bijan, Mihir A. Parikh et Sameer Verma. « Adaptive decision support systems ». Decision Support Systems 20, no 4 (août 1997) : 297–315. http://dx.doi.org/10.1016/s0167-9236(97)00014-6.

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Burns, N. D. « Manufacturing decision support systems ». Materials & ; Design 17, no 5-6 (janvier 1996) : 304–5. http://dx.doi.org/10.1016/s0261-3069(97)82748-3.

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Wyatt, Jeremy C. « 9. Decision support systems ». Journal of the Royal Society of Medicine 93, no 12 (décembre 2000) : 629–33. http://dx.doi.org/10.1177/014107680009301206.

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Eom, Sean, Sang Lee, Chellappa Somarajan et Eyong Kim. « Decision support systems applications ». OR Insight 10, no 2 (avril 1997) : 18–32. http://dx.doi.org/10.1057/ori.1997.9.

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Jacob, Varghese S., et Hasan Pirkul. « Organizational decision support systems ». International Journal of Man-Machine Studies 36, no 6 (juin 1992) : 817–32. http://dx.doi.org/10.1016/0020-7373(92)90074-u.

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26

Hornberger, John, et Mary Kane Goldstein. « Clinical Decision-support Systems : ». Medical Decision Making 20, no 1 (janvier 2000) : 130–31. http://dx.doi.org/10.1177/0272989x0002000116.

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Nour, Mohamed A., et David (Chi-Chung) Yen. « Group decision support systems ». Information & ; Management 23, no 2 (août 1992) : 55–64. http://dx.doi.org/10.1016/0378-7206(92)90008-4.

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Gray, Paul. « Group decision support systems ». Decision Support Systems 3, no 3 (septembre 1987) : 233–42. http://dx.doi.org/10.1016/0167-9236(87)90178-3.

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Gottinger, Hans W., et Peter Weimann. « Intelligent decision support systems ». Decision Support Systems 8, no 4 (août 1992) : 317–32. http://dx.doi.org/10.1016/0167-9236(92)90053-r.

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Cooney, Timothy M. « Software : Decision Support Systems ». Journal of Forestry 84, no 1 (1 janvier 1986) : 13–14. http://dx.doi.org/10.1093/jof/84.1.13.

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31

Watson, Hugh J., Astrid Lipp, Pamela Z. Jackson, Abdelhafid Dahmani et William B. Fredenberger. « Organizational Support for Decision Support Systems ». Journal of Management Information Systems 5, no 4 (mars 1989) : 87–109. http://dx.doi.org/10.1080/07421222.1989.11517841.

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32

Wachowicz, Tomasz. « DECISION SUPPORT IN SOFTWARE SUPPORTED NEGOTIATIONS ». Journal of Business Economics and Management 11, no 4 (31 décembre 2010) : 576–97. http://dx.doi.org/10.3846/jbem.2010.28.

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In this paper we consider the idea of negotiations conducted by means of the software support tools. We present the advantages of the negotiation support systems discussing their different functions and typologies focusing later on the possibilities of decision support they can give to the negotiating parties in all negotiation phases. After presenting the most popular solutions we introduce also two of our own procedures that can be applied in the pre‐negotiation phase for eliciting negotiators’ preferences and building the offers’ scoring systems for the parties. The first one is based on the Hammond, Keeney and Raiffa's procedure of even swaps, while the second derives from the Roy's ELECTRE‐TRI. Both of them can be easily applied as the analytic engines in electronic negotiation systems replacing the classical additive scoring systems. We discuss also the issue of using different scoring systems in the successive negotiation phases.
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33

Knight, Jonathan D., et John D. Mumford. « Decision Support Systems in Crop Protection ». Outlook on Agriculture 23, no 4 (décembre 1994) : 281–85. http://dx.doi.org/10.1177/003072709402300408.

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All farmers and growers have at some time faced the decision of whether to control a pest in their crop. In order to make the correct decision the farmer needs access to, and an understanding of, sufficient information relevant to such pest problems. Decision support systems are able to help farmers make these difficult decisions by providing information in an easily understandable and quickly accessed form. The increasing use of computers by farmers for record-keeping and business management is putting the hardware necessary for the implementation of these systems onto more and more farms. The scarcity of expert advice, increasingly complex decisions and reduced economic margins all increase the importance of making the right pest management decision at the right time. It is against this background that decision support systems have an important role to play in the fight against losses caused by pests and diseases.
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Mashli Aina, Ahmed Abdulatef. « Developing Decision Support Capabilities through Use of Management Information Systems ». International Journal of Management Science and Business Administration 1, no 9 (2015) : 46–51. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.19.1005.

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Management Information Systems (MIS) are widely used in big corporations to collect, organize, filter and present data, resulting in more effective decision-making. Through use of MIS, individuals shape the way how they make decisions. In our study, we posit that utilization of MIS in the decision-making process impacts the decision support capabilities of systems. The use of MIS develops speed, quality, and other features of decision-making. Moreover, improvement in decision-making is aimed to ensure customer satisfaction and good business results. All MIS strategies should therefore be tailored in a way that the all business goals are achieved.
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Huryk, Laurie A. « Information Systems and Decision Support Systems ». AJN, American Journal of Nursing 112, no 1 (janvier 2012) : 62–65. http://dx.doi.org/10.1097/01.naj.0000410365.51061.81.

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Sydow, A., R. Straubel et K. Bellmann. « Decision Support Systems for Environmental Systems ». IFAC Proceedings Volumes 20, no 12 (septembre 1987) : 443–47. http://dx.doi.org/10.1016/s1474-6670(17)55672-4.

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37

O’Sullivan, Dympna, Paolo Fraccaro, Ewart Carson et Peter Weller. « Decision time for clinical decision support systems ». Clinical Medicine 14, no 4 (août 2014) : 338–41. http://dx.doi.org/10.7861/clinmedicine.14-4-338.

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Hersh, M. A. « Sustainable decision making and decision support systems ». Computing & ; Control Engineering Journal 9, no 6 (1 décembre 1998) : 289–95. http://dx.doi.org/10.1049/cce:19980610.

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39

Lee, Daniel T. « Expert Decision-support Systems for Decision-making ». Journal of Information Technology 3, no 2 (juin 1988) : 85–94. http://dx.doi.org/10.1057/jit.1988.16.

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Lee, Daniel T. « Expert Decision-support Systems for Decision-making ». Journal of Information Technology 3, no 2 (juin 1988) : 85–94. http://dx.doi.org/10.1177/026839628800300204.

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Computers have made tremendous contributions towards transactional processing. However, the highest pay-off the computer can make is not in transactional processing but in decision-making. Recently, expert systems have just begun to be used in the decision-making process. Individual technologies alone are inadequate for an effective decision support. The purpose of this paper is to investigate the related issues in decision support and to develop an expert decision support system (EDSS) for combining decision support systems and expert systems into a unified whole for decision support. The emphasis will be on developing a DSS/ES model which can be used to integrate the traditional DSS database and ES knowledge-base for building a user-friendly EDSS.
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Harmon, Joel I. « Tactical decision making and decision support systems ». New Directions for Institutional Research 1986, no 49 (1986) : 17–27. http://dx.doi.org/10.1002/ir.37019864904.

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Liu, Shaofeng, Alex H. B. Duffy, Robert Ian Whitfield et Iain M. Boyle. « Integration of decision support systems to improve decision support performance ». Knowledge and Information Systems 22, no 3 (6 février 2009) : 261–86. http://dx.doi.org/10.1007/s10115-009-0192-4.

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Palestini, Luigi. « Communication and Decision Support Systems ». International Journal of Safety and Security Engineering 11, no 4 (31 août 2021) : 397–407. http://dx.doi.org/10.18280/ijsse.110413.

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In emergencies, assessment and communication activities are particularly important for the support of the top decision-making bodies, to evaluate “just in time” the best actions to be taken. The multiple problems to be solved require specific skills in different areas. Upon the occurrence of a calamity, the authorities must answer questions such as “is a given place safe from the threat (e.g., an oncoming flood)?”, that’s why today knowledge of tools that can support decision-making is increasingly necessary: the so-called Decision Support Systems (DSS), software that allow users to improve situation assessment, helping all those who must make strategic decisions. Hand in hand with the growing interest in DSS there is an increasing use of communication systems based on IT. First responders know that to face an emergency everything must be prepared and planned, also communication. In fact, DSS and voice/data transmission systems are often integrated into a single system, as proposed by the European projects FIRE IN and IN PREP, because managing information is crucial for carrying out rescue activities in the best possible way. This work describes the impact of new technologies on rescue and emergency management in Italy and Europe, highlighting the challenges associated with their use.
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Walsh, Seán, Evelyn E. C. de Jong, Janna E. van Timmeren, Abdalla Ibrahim, Inge Compter, Jurgen Peerlings, Sebastian Sanduleanu et al. « Decision Support Systems in Oncology ». JCO Clinical Cancer Informatics, no 3 (décembre 2019) : 1–9. http://dx.doi.org/10.1200/cci.18.00001.

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Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Sauter, Vicki L., Srikanth Mudigonda, Ashok Subramanian et Ray Creely. « Visualization-Based Decision Support Systems ». International Journal of Decision Support System Technology 3, no 1 (janvier 2011) : 1–20. http://dx.doi.org/10.4018/jdsst.2011010101.

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Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.
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Малых, Владимир Леонидович. « Decision support systems in medicine ». Program Systems : Theory and Applications 10, no 2 (2019) : 155–84. http://dx.doi.org/10.25209/2079-3316-2019-10-2-155-184.

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Современная медицина осваивает системы поддержки принятия решений (СППР). Анализируя российские и зарубежные источники, работа выявляет основные барьеры на пути создания СППР для медицины. Приводятся возможные подходы к преодолению концептуальных барьеров, предлагается возможное комплексное решение проблемы. Рассматривается гибридная модель СППР широкого класса для медицины. Результаты могут быть использованы разработчиками ИТ для построения СППР на основе научной и эмпирической компонент медицинских знаний.
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Gray, Paul, et Omar A. El Sawy. « Implications for Decision Support Systems ». Journal of Decision Systems 19, no 4 (janvier 2010) : 377–87. http://dx.doi.org/10.3166/jds.19.377-387.

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Abu-Hanna, A., et B. Nannings. « Characterizing Decision Support Telemedicine Systems ». Methods of Information in Medicine 45, no 05 (2006) : 523–27. http://dx.doi.org/10.1055/s-0038-1634113.

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Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.
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Ramnarayan, P. « Paediatric clinical decision support systems ». Archives of Disease in Childhood 87, no 5 (1 novembre 2002) : 361–62. http://dx.doi.org/10.1136/adc.87.5.361.

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Ba, Sulin, et Andrew B. Whinston. « Challenges for Decision Support Systems ». IEEJ Transactions on Electronics, Information and Systems 114, no 3 (1994) : 295–309. http://dx.doi.org/10.1541/ieejeiss1987.114.3_295.

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