A decision support framework for prediction of avian influenza

Abstract For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

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20th Anniversary Update of the Ottawa Decision Support Framework Part 1: A Systematic Review of the Decisional Needs of People Making Health or Social Decisions

Medical Decision Making ◽

10.1177/0272989x20936209 ◽

2020 ◽

Vol 40 (5) ◽

pp. 555-581 ◽

Cited By ~ 3

Author(s):

Lauren Hoefel ◽

Annette M. O’Connor ◽

Krystina B. Lewis ◽

Laura Boland ◽

Lindsey Sikora ◽

...

Keyword(s):

Systematic Review ◽

Decision Support ◽

Family Involvement ◽

Information Overload ◽

Decision Makers ◽

Difficult Decision ◽

Social Decisions ◽

Patient Decision ◽

Decision Support Framework ◽

Ottawa Decision Support Framework

Background. The Ottawa Decision Support Framework (ODSF) has been used for 20 years to assess and address people’s decisional needs. The evidence regarding ODSF decisional needs has not been synthesized. Objectives. To synthesize evidence from ODSF-based decisional needs studies, identify new decisional needs, and validate current ODSF decisional needs. Methods. A mixed-studies systematic review. Nine electronic databases were searched. Inclusion criteria: studies of people’s decisional needs when making health or social decisions for themselves, a child, or a mentally incapable person, as reported by themselves, families, or practitioners. Two independent authors screened eligibility, extracted data, and quality appraised studies using the Mixed Methods Appraisal Tool. Data were analyzed using narrative synthesis. Results. Of 4532 citations, 45 studies from 7 countries were eligible. People’s needs for 101 unique decisions (85 health, 16 social) were reported by 2857 patient decision makers ( n = 36 studies), 92 parent decision makers ( n = 6), 81 family members ( n = 5), and 523 practitioners ( n = 21). Current ODSF decisional needs were reported in 2 to 40 studies. For 6 decisional needs, there were 11 new (manifestations): 1) information (overload, inadequacy regarding others’ experiences with options), 2) difficult decisional roles (practitioner, family involvement, or deliberations), 3) unrealistic expectations (difficulty believing outcome probabilities apply to them), 4) personal needs (religion/spirituality), 5) difficult decision timing (unpredictable), and 6) unreceptive decisional stage (difficulty accepting condition/need for treatment, powerful emotions limiting information processing, lacking motivation to consider delayed/unpredictable decisions). Limitations. Possible publication bias (only peer-reviewed journals included). Possible missed needs (non-ODSF studies, patient decision aid development studies, 3 ODSF needs added in 2006). Conclusion. We validated current decisional needs, identified 11 new manifestations of 6 decisional needs, and recommended ODSF revisions.

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Safe Vehicle Trajectory Planning in an Autonomous Decision Support Framework for Emergency Situations

Applied Sciences ◽

10.3390/app11146373 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6373

Author(s):

Wei Xu ◽

Rémi Sainct ◽

Dominique Gruyer ◽

Olivier Orfila

Keyword(s):

Decision Support ◽

Trajectory Planning ◽

Autonomous Driving ◽

General Concept ◽

Elastic Band ◽

Emergency Situations ◽

Autonomous Decision ◽

Decision Support Framework ◽

Control Command ◽

Eu Project

For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios.

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Decision support framework for location selection and disaster relief network design

Journal of Humanitarian Logistics and Supply Chain Management ◽

10.1108/jhlscm-11-2016-0040 ◽

2017 ◽

Vol 7 (3) ◽

pp. 222-245 ◽

Cited By ~ 12

Author(s):

Giuseppe Timperio ◽

Gajanan Bhanudas Panchal ◽

Avinash Samvedi ◽

Mark Goh ◽

Robert De Souza

Keyword(s):

Decision Support ◽

Network Design ◽

Disaster Relief ◽

Real Life ◽

Humanitarian Logistics ◽

Decision Makers ◽

Use Case ◽

Distribution Centers ◽

Content Type ◽

Decision Support Framework

Purpose The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia. Design/methodology/approach An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used. Findings For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado. Research limitations/implications The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters. Practical implications The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced. Originality/value It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.

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A Novel Decision Support Framework for Computing Expected Utilities from Linguistic Evaluations

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488517500441 ◽

2017 ◽

Vol 25 (06) ◽

pp. 1005-1018 ◽

Cited By ~ 3

Author(s):

Debora Di Caprio ◽

Francisco J. Santos-Arteaga ◽

Madjid Tavana

Keyword(s):

Decision Support ◽

Decision Makers ◽

Decision Support Framework ◽

Probability Mass

The increase in the amount and variety of evaluations provided by the users of different websites regarding the products displayed is becoming an increasingly familiar scenario. That is, decision makers (DMs) constantly receive linguistic evaluations (LEs) from unknown evaluators when considering different choice alternatives. The imprecision of the LEs and the fact that the evaluators may have biased interests when describing a product must be considered by the DMs when computing their expected utilities. We define a Bayesian-updated probability (BUP) function that accounts for the fuzziness inherent in the LEs and the reputation of the evaluator to represent the beliefs of DMs. The proposed BUP process allows the DMs to subjectively adjust the probability mass that is shifted across evaluation intervals when updating their beliefs and computing their corresponding expected utilities. We illustrate the behavior of the BUP function numerically and describe potential decision support applications.

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Data Warehouse Facilitating Evidence-Based Medicine

Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development ◽

10.4018/978-1-60566-748-5.ch008 ◽

2010 ◽

pp. 174-207 ◽

Cited By ~ 2

Author(s):

Nevena Stolba ◽

Tho Manh Nguyen ◽

A Min Tjoa

Keyword(s):

Health Care ◽

Decision Support ◽

Decision Support Systems ◽

Knowledge Base ◽

Data Warehouse ◽

Support Systems ◽

Medical Data ◽

Decision Makers ◽

Evidence Based ◽

Healthcare Decision

In the past, much effort of healthcare decision support systems were focused on the data acquisition and storage, in order to allow the use of this data at some later point in time. Medical data was used in static manner, for analytical purposes, in order to verify the undertaken decisions. Due to the immense volumes of medical data, the architecture of the future healthcare decision support systems focus more on interoperability than on integration. With the raising need for the creation of unified knowledge base, the federated approach to distributed data warehouses (DWH) is getting increasing attention. The exploitation of evidence-based guidelines becomes a priority concern, as the awareness of the importance of knowledge management rises. Consequently, interoperability between medical information systems is becoming a necessity in modern health care. Under strong security measures, health care organizations are striking to unite and share their (partly very high sensitive) data assets in order to achieve a wider knowledge base and to provide a matured decision support service for the decision makers. Ontological integration of the very complex and heterogeneous medical data structures is a challenging task. The authors’ objective is to point out the advantages of the deployment of a federated data warehouse approach for the integration of the wide range of different medical data sources and for distribution of evidence-based clinical knowledge, to support clinical decision makers, primarily clinicians at the point of care.

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Presentation of precedents in decision support system knowledge base under diagnosis of portal cranes on the basis of the rough sets theory

Shipbuilding and marine infrastructure ◽

10.15589/smi20140109 ◽

2014 ◽

Vol 1 (1) ◽

Author(s):

Igor I. Kovalenko ◽

Anton V. Melnik

Keyword(s):

Decision Support ◽

Decision Support System ◽

Knowledge Base ◽

Support System ◽

Rough Sets ◽

Rough Sets Theory ◽

Sets Theory ◽

System Knowledge

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Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽

10.1609/aimag.v40i3.4810 ◽

2019 ◽

Vol 40 (3) ◽

pp. 41-57

Author(s):

Manisha Mishra ◽

Pujitha Mannaru ◽

David Sidoti ◽

Adam Bienkowski ◽

Lingyi Zhang ◽

...

Keyword(s):

Decision Support ◽

Situational Awareness ◽

Feature Space ◽

Human Interaction ◽

Superior Performance ◽

Uncertain Environments ◽

Cognitive States ◽

Contextual Elements ◽

Human Operators ◽

Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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Developing Dynamic Reference Models and a Decision Support Framework for Southeastern Ecosystems: An Integrated Approach

10.21236/ada626412 ◽

2015 ◽

Author(s):

L. K. Kirkman ◽

John K. Hiers A. ◽

L. L. Smith ◽

L. M. Conner ◽

S. L. Zeigler ◽

...

Keyword(s):

Decision Support ◽

Integrated Approach ◽

Reference Models ◽

Decision Support Framework

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A Multi-Criteria Decision Support Concept for Selecting the Optimal Contractor

Applied Sciences ◽

10.3390/app11041660 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1660 ◽

Cited By ~ 1

Author(s):

Ivan Marović ◽

Monika Perić ◽

Tomaš Hanak

Keyword(s):

Decision Making ◽

Decision Support ◽

Project Performance ◽

Decision Makers ◽

Construction Project Management ◽

Analytic Hierarchy ◽

Conflicting Demands ◽

Decision Making Problem ◽

Scientific Approach ◽

Hierarchy Process

A way to minimize uncertainty and achieve the best possible project performance in construction project management can be achieved during the procurement process, which involves selecting an optimal contractor according to “the most economically advantageous tender.” As resources are limited, decision-makers are often pulled apart by conflicting demands coming from various stakeholders. The challenge of addressing them at the same time can be modelled as a multi-criteria decision-making problem. The aim of this paper is to show that the analytic hierarchy process (AHP) together with PROMETHEE could cope with such a problem. As a result of their synergy, a decision support concept for selecting the optimal contractor (DSC-CONT) is proposed that: (a) allows the incorporation of opposing stakeholders’ demands; (b) increases the transparency of decision-making and the consistency of the decision-making process; (c) enhances the legitimacy of the final outcome; and (d) is a scientific approach with great potential for application to similar decision-making problems where sustainable decisions are needed.

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