Model building for decision support, data analysis and scientific discovery

2015 ◽  
pp. 480-501
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Model Building ◽  
Scientific Discovery

Application of Multidimensional Data Analysis in Power Marketing Decision Support System Based on Big Data

2016 ◽  
Vol 9 (10) ◽  
pp. 295-304
Author(s):  
Sun Zhineng ◽  
Yu Fang ◽  
Zhou Kaihe ◽  
Li Jiangang ◽  
Fang Yunhui
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support System ◽  
Support System ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Marketing Decision

DREAM: a shell-like software system for medical data analysis and decision support

1993 ◽  
Vol 40 (2) ◽  
pp. 73-81 ◽  
Author(s):  
Ludmila Kuncheva ◽  
Kostadinka Andreeva
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Medical Data ◽  
Software System

Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes

2018 ◽  
Vol 59 (6) ◽  
pp. 1024-1033 ◽  
Author(s):  
Mustafa Ozkaynak ◽  
Blaine Reeder ◽  
Cynthia Drake ◽  
Peter Ferrarone ◽  
Barbara Trautner ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Nursing Homes ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Staff Members ◽  
Design And Implementation ◽  
Multiple Levels

Abstract Background and Objectives Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. Research Design and Methods We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. Results The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members’ preferences on work activities. We also identified workflow-related differences between the two settings. Discussion and Implications Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

PROLOG-Based Decision Support for Data Analysis in Marketing

1988 ◽  
pp. 19-34 ◽  
Author(s):  
I. Böckenholt ◽  
M. Both ◽  
W. Gaul
Keyword(s):  
Decision Support ◽  
Data Analysis

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

2008 ◽  
pp. 391-399
Author(s):  
Markos G. Tsipouras ◽  
Themis P. Exarchos ◽  
Dimitrios I. Fotiadis ◽  
Aris Bechlioulis ◽  
Katerina K. Naka
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Analysis ◽  
Cardiovascular Diseases ◽  
Medical Information ◽  
Medical Decision ◽  
Fuzzy Modelling ◽  
Analysis Techniques ◽  
Clinical Environments ◽  
Artery Disease

This article addresses the decision support regarding cardiovascular diseases, using computer-based methods, focusing on the coronary artery disease (CAD) diagnosis and on the prediction of clinical restenosis in patients undergoing angioplasty. Methods reported in the literature are reviewed with respect to (i) the medical information that are employing in order to reach the diagnosis and (ii) the data analysis techniques used for the creation of the CDSSs. In what concerns medical information, easily and noninvasively-obtained data present several advantages compared to other types of data, while data analysis techniques that are characterized by transparency regarding their decisions are more suitable for medical decision making. A recently developed approach that complies with the above requirements is presented. The approach is based on data mining and fuzzy modelling. Using this approach, one CDSS has been developed for each of the two cardiovascular problems mentioned above. These CDSSs are extensively evaluated and comments about the discovered knowledge are provided by medical experts. The later is of great importance in designing and evaluating CDSSs, since it allows them to be integrated into real clinical environments.

scholarly journals Two-Envelope System for Consultant Selection using Weighted Sum Model

2018 ◽  
Vol 7 (4.27) ◽  
pp. 121
Author(s):  
Mohd Faizal Bin Omar ◽  
Bambang Trigunarsyah ◽  
Johnny Wong
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Model Building ◽  
Classical Problem ◽  
Quality Criteria ◽  
Weighted Sum ◽  
Decision Support Model ◽  
User Requirement ◽  
Study Approach ◽  
Technical Department

Consultant Selection is one of a classical problem in Multi Criteria Decision Making (MCDM). Most of the literature in Operation Research only concentrates on model building rather than developing an inclusive analytic tool that extends to a Decision Support System (DSS). In this paper, we deploy a case study approach to understand the user requirement for DSS development. We observe the process of consultant selection and the decision making at one of the technical department which involve in the infrastructure project in Malaysia. A two-envelope system and a simple Weighted Sum Model are currently in use. We demonstrate the abstraction and application based on two case projects. Sensitivity analysis is also performed and the result shows that the decision changed if it is solely based on fees or with minimal quality criteria.  Finally, we gather the findings from the organizational flows, user modelling and decision making process in order to benchmark with our future works. This will helps us to better understand and develop an improved decision support model or tools for consultant selection problem. 

Sign in / Sign up

Export Citation Format

Share Document