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

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.

Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

2021 ◽  
pp. 175-204
Author(s):  
S. N. Kumar ◽  
A. Lenin Fred ◽  
L. R. Jonisha Miriam ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Medical Data

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

scholarly journals Computing support for advanced medical data analysis and imaging

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Wojciech Wiślicki ◽  
Tomasz Bednarski ◽  
Piotr Białas ◽  
Eryk Czerwiński ◽  
Łukasz Kapłon ◽  
...  
Keyword(s):  
Data Analysis ◽  
Medical Data ◽  
Computing Support

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 Daisy: Data analysis integrated software system for X-ray experiments

2021 ◽  
Vol 251 ◽  
pp. 04020
Author(s):  
Yu Hu ◽  
Ling Li ◽  
Haolai Tian ◽  
Zhibing Liu ◽  
Qiulan Huang ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Energy ◽  
Software System ◽  
Energy Photon ◽  
X Ray ◽  
High Throughput Data ◽  
Extensive Range ◽  
Algorithmic Problems ◽  
Integrated Software ◽  
Photon Source

Daisy (Data Analysis Integrated Software System) has been designed for the analysis and visualisation of X-ray experiments. To address the requirements of the Chinese radiation facilities community, spanning an extensive range from purely algorithmic problems to scientific computing infrastructure, Daisy sets up a cloud-native platform to support on-site data analysis services with fast feedback and interaction. Furthermore, the plug-in based application is convenient to process the expected high throughput data flow in parallel at next-generation facilities such as the High Energy Photon Source (HEPS). The objectives, functionality and architecture of Daisy are described in this article.

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