scholarly journals Integrating machine learning and decision support in tactical decision-making in rugby union

2020 ◽  
pp. 1-12
Author(s):  
Neil Watson ◽  
Sharief Hendricks ◽  
Theodor Stewart ◽  
Ian Durbach
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Rugby Union ◽  
Tactical Decision

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Computerised Decision Support for Women's Health Informatics

2009 ◽  
pp. 302-314
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

An Assessment of Federated Machine Learning for Translational Research

2020 ◽  
pp. 123-142
Author(s):  
Manoj A. Thomas ◽  
Diya Suzanne Abraham ◽  
Dapeng Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Translational Research ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
High Quality ◽  
Intermediate Data ◽  
Translational Informatics ◽  
Data Analytic

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This chapter builds upon TR concepts and aims to advance the use of machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is described to aggregate multiple ML endpoints, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. The chapter illustrates contributions to the advancement of FML and TI.

Perspectives on Decision Analysis for Decision Support System Design

1993 ◽  
Vol 37 (16) ◽  
pp. 1101-1101 ◽  
Author(s):  
Barbara J. Barnett
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
System Design ◽  
Support System ◽  
Information Organization ◽  
Human Decision ◽  
Tactical Decision ◽  
Decision Making Behavior

This symposium addresses the characterization of human decision making within a complex environment for the purpose of developing improved decision support systems. All of the work presented in this symposium was conducted under a Navy research program entitled “Tactical Decision Making Under Stress” (TADMUS). The overall objective of the TADMUS program is to improve tactical decision making of anti-air warfare (AAW) crew members within the Aegis cruiser's combat information center (CIC) under conditions of stress and uncertainty. The unique aspect of this effort is that each presentation addresses decision making behavior, within a single domain, from a different perspective. The goal of each effort is to characterize some aspect of expert decision making performance within the AAW task environment, and to make recommendations for the resulting decision support system design based upon these characterizations. The result is a multi-faceted, human-centered approach to information organization and interface display design for a decision support system.

scholarly journals Artificial intelligence for team sports: a survey

2019 ◽  
Vol 34 ◽  
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Outcome Prediction ◽  
Team Sports ◽  
Fantasy Sports ◽  
Sports Teams ◽  
Injury Prediction ◽  
Tactical Decision ◽  
Research Questions

Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.

scholarly journals Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

2020 ◽  
Author(s):  
Victor Silva ◽  
Amanda Days Ramos Novo ◽  
Damires Souza ◽  
Alex Rêgo
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area ◽  
Review Of The Literature

Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

A Psychological Approach to Evaluating Decision Support Systems

1983 ◽  
Vol 27 (6) ◽  
pp. 492-495
Author(s):  
Leonard Adelman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Air Force ◽  
Support System ◽  
Test Construction ◽  
Potential Utility ◽  
Psychological Approach ◽  
Tactical Decision

This paper provides an overview of a standardized questionnaire built around the foundation of a multi-attribute utility assessment hierarchy, and utilizing psychometric guidelines for test construction, to measure the potential utility of five decision support system (DSS) prototypes developed for aiding different areas of U.S. Air Force tactical decision making.

scholarly journals A Novel Decision Support System for Long-Term Management of Bridge Networks

2021 ◽  
Vol 11 (13) ◽  
pp. 5928
Author(s):  
Enes Karaaslan ◽  
Ulas Bagci ◽  
Necati Catbas
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Integration ◽  
Support System ◽  
Machine Learning Algorithms ◽  
Structural Safety ◽  
Bridge Management ◽  
Wide Range

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.

Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

2017 ◽  
Vol 67 (5) ◽  
pp. 529 ◽  
Author(s):  
Petr Stodola ◽  
Jan Mazal
Keyword(s):  
Decision Making ◽  
Simulated Annealing ◽  
Decision Support ◽  
Probabilistic Methods ◽  
Ant Colony Optimisation ◽  
Area Of Interest ◽  
Metaheuristic Methods ◽  
Military Tactics ◽  
Decision Making Processes ◽  
Tactical Decision

<p>The model of optimal cooperative reconnaissance as a part of the tactical decision support system to aid commanders in their decision-making processes is presented. The model represents one of the models of military tactics implemented in the system to plan the ground reconnaissance operation for the commander optimally. The main goal of the model is to explore the area of interest by multiple military elements (scouts, UAVs, UGVs) as quickly as possible. A metaheuristic solution to this problem which combines two probabilistic methods: simulated annealing and the ant colony optimisation algorithm is proposed. In the first part of this study, the optimal cooperative reconnaissance problem is formulated. Then, metaheuristic solution, which is composed of three independent steps, is presented. Finally, experiments are conducted to verify the approach to this problem.</p>

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