China
Africa
Explore chapters and articles related to this topic
Supporting Decision Making During a Pandemic: Influence of Stress, Analytics, Experts, and Decision Aids
Published in Jay Liebowitz, The Business of Pandemics, 2020
Gloria Phillips-Wren, Jean-Charles Pomerol, Karen Neville, Frédéric Adam
Decision support technologies take many forms, from traditional and advanced intelligent decision support systems to modern concepts of analytics. Traditional decision support takes inputs from the environment and human users, processes those inputs through mathematical and statistical models, and provides outputs in terms of one or more alternatives. The user interacts with the system to provide additional data or information, modify the selection of models, and accept or reject alternatives. Intelligent decision support systems augment the system with various types of artificial intelligence and machine learning so that the system can be smarter, comprehensive, adaptive, and anticipatory in support of the decision maker. All of these systems intend to improve the quality of semi-structured and unstructured decisions.
Green Healthcare for Smart Cities
Published in Pradeep Tomar, Gurjit Kaur, Green and Smart Technologies for Smart Cities, 2019
Prabhjot Singh, Varun Dixit, Jaspreet Kaur
Intelligent decision support systems (IDSS) can be defined as a system that helps in decision-making by the use of AI and machine-learning technologies (Chang et al. 1994). These systems generally use a hybrid approach, utilizing simple rule-based engines, knowledge-based engines and neural network engines. It performs an interpretive analysis of large-scale data with intelligent and knowledge-based methods.
Artificial Intelligence and Inpatients' Risk Vulnerability Assessment: Trends, Challenges, and Applications
Published in Vineet Kansal, Raju Ranjan, Sapna Sinha, Rajdev Tiwari, Nilmini Wickramasinghe, Healthcare and Knowledge Management for Society 5.0, 2021
Chinedu I. Ossai, Nilmini Wickramasinghe
The application of knowledge management to health care is growing in importance and significance to assist in contending with the generation of voluminous amounts of data that now are evident in numerous contexts in health care delivery. Within knowledge management, a recognized critical aspect is artificial intelligence (AI) (Wickramasinghe et al., 2009). The role of AI in enhancing intelligent decision support in health care is becoming more important, especially given the ubiquitous applications for seamless predictions of key patient outcomes. Patients on admission to hospitals are faced with the possibility of incurring numerous risks such as falls, hospital-acquired pressure injury (HAPI), hospital-acquired malnutrition (Mal), and venous thromboembolism (VTE). Unfortunately, the predisposition of inpatients to these risks impacts their health outcomes and the cost of managing health care around the world. Even though the risks associated with health care cannot be eliminated, they can be minimized to reduce the adverse effects they have on patients. So, the increasing awareness of efficiency in the health care context has resulted in the application of AI in big and complicated data analysis to provide better techniques for diagnosis and treatment of different health conditions (Ellahham et al., 2020). Consequently, properly implemented AI techniques have improved intelligent decision support systems for medication management and patient stratification due to minimal prediction errors (Choudhury & Asan, 2020). Other notable applications of AI in the health care context include triage (Kim et al., 2018) and wearable devices for remote monitoring and analysis of physiological conditions (Chan et al., 2012;Shi et al., 2020). The role of AI in mental health has been attributed to identifying individuals in an emotional crisis that need psychoeducational supports that include emergency assistance (Fonseka et al., 2019). It has also been a tool employed for enhancing decision support for clinical radiology practice to improve quality assurance of experts and interoperability of algorithms (Reyes et al., 2020).
A generic knowledge management approach towards the development of a decision support system
Published in International Journal of Production Research, 2021
Oussama Meski, Farouk Belkadi, Florent Laroche, Mathieu Ritou, Benoit Furet
Along the technological evolutions, and the development of research on DSS, this field greatly evolved and several approaches and subfields emerged over time. Arnott and Pervan (2005) deals with DSS and defines the main subfields and their evolution over time. It is hence possible to describe some types of DSS: Personal Decision Support Systems: as the name implies, these are often developed for a single manager or a group of independent managers.Group Support Systems: are developed to improve the work and collaboration among a working group. The main objective of this system is to encourage decision-making based on all the suggestions proposed by the various members of the group.Negotiation Support Systems: this type of system uses computer technologies to facilitate negotiation. There are two categories of these systems: problem-oriented and process-oriented.Intelligent Decision Support Systems: this category of system introduces the use of artificial intelligence in decision support. They are classified into two generations: the first is rule-based systems and the second generation includes genetic algorithms, fuzzy logic and neural networks.Knowledge Management-Based DSS: these systems allow the taking based on the capitalisation of knowledge and the development of knowledge repositories. Also the manipulation, transfer and reuse of knowledge, for the purpose of decision-making and thus the creation of new knowledge.