A study on investigation of the usage of decision support systems and evidence-based medicine relations via machine learning algorithms

DECISION ◽  
2021 ◽  
Vol 48 (3) ◽  
pp. 249-259
Author(s):  
Özel Sebetci ◽  
Hasan Yildirim
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Evidence Based Medicine ◽  
Support Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Evidence Based ◽  
Based Medicine

scholarly journals Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

2021 ◽  
Vol 9 (3) ◽  
pp. 271
Author(s):  
Luca Braidotti ◽  
Marko Valčić ◽  
Jasna Prpić-Oršić
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Machine Learning Algorithms ◽  
Damage Scenario ◽  
Passenger Ships ◽  
Bagged Decision Trees ◽  
Emergency Decision ◽  
New Generation

Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision 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.

Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

2017 ◽  
Vol 50 (4) ◽  
pp. 597-623 ◽  
Author(s):  
Saima Safdar ◽  
Saad Zafar ◽  
Nadeem Zafar ◽  
Naurin Farooq Khan
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis

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

Decision Support for Mental Health

2014 ◽  
Vol 4 (2) ◽  
pp. 53-65
Author(s):  
Jan Kalina ◽  
Jana Zvárová
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Decision Making Process ◽  
Basic Principles ◽  
Points Of View ◽  
Concept Of Information ◽  
Based Medicine ◽  
General Perspective

Decision support systems represent an important tool offering assistance with the decision making process in a variety of applications. This paper starts with recalling the basic principles and structure of decision support systems in medicine from a general perspective. Their effect in terms of both potential and limitations for finding the diagnosis, prognosis and therapy are overviewed from the points of view of health care effectiveness and patient safety. The authors are particularly interested in the specialty field of psychiatry. They discuss its specific challenges and analyze the slower penetration of telemedicine tools to psychiatry compared to other clinical fields. Finally, they claim that the development of decision support systems play a key role in the development of the concept of information-based medicine in general as well as to the particular context of information-based psychiatry.

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