Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2353 ◽  
Author(s):  
Eta S. Berner ◽  
Bunyamin Ozaydin
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

scholarly journals Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

2021 ◽  
Vol 9 (11) ◽  
pp. 1303
Author(s):  
Luca Braidotti ◽  
Jasna Prpić-Oršić ◽  
Marko Valčić
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Decision Support ◽  
Decision Support Systems ◽  
Random Forests ◽  
Support Systems ◽  
Probability Distributions ◽  
New Generation ◽  
Main Effects ◽  
Further Development

Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.

scholarly journals Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2354 ◽  
Author(s):  
Lisa Licitra ◽  
Annalisa Trama ◽  
Hykel Hosni
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

Benefits and Risks of Machine Learning Decision Support Systems—Reply

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2356 ◽  
Author(s):  
Federico Cabitza ◽  
Raffaele Rasoini ◽  
Gian Franco Gensini
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems
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