An Aspect of Decision Making in Rough Non-deterministic Information Analysis

2009 ◽  
pp. 527-536 ◽  
Author(s):  
Hiroshi Sakai ◽  
Kohei Hayashi ◽  
Hiroshi Kimura ◽  
Michinori Nakata
Keyword(s):  
Decision Making ◽  
Information Analysis

Stable rule extraction and decision making in rough non-deterministic information analysis

2011 ◽  
Vol 8 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Hiroshi Sakai ◽  
Hitomi Okuma ◽  
Michinori Nakata ◽  
Dominik Ślȩzak
Keyword(s):  
Decision Making ◽  
Rule Extraction ◽  
Information Analysis

scholarly journals (A226) Establishing a Framework for Synchronizing Critical Decision Making with Information Analysis during a Health/Medical Emergency

2011 ◽  
Vol 26 (S1) ◽  
pp. s61-s61 ◽  
Author(s):  
J. Paturas ◽  
J. Pelazza ◽  
R. Smith
Keyword(s):  
Decision Making ◽  
Emergency Preparedness ◽  
Disaster Response ◽  
The United States ◽  
Medical Emergency ◽  
Incident Management ◽  
Local Health ◽  
Information Analysis ◽  
Incident Command ◽  
State And Local

BackgroundThe Yale New Haven Center for Emergency Preparedness and Disaster Response (YNH-CEPDR) has worked in the United States with state and local health and medical organizations to evaluate critical decision making activities and to develop decision making tools and protocols to enhance decision making in a time sensitive environment. YNH-CEPDR has also worked with international organizations and US federal agencies to support situational awareness activities in simulated and real world events.ObjectivesDuring this session YNH-CEPDR will share the best practices from recent events such as the H1N1 response and the Haiti Earthquake. Participants will be engaged in discussions regarding overall framework for successful information collection, analysis and dissemination to support decision making based on these experiences. This session will also incorporate concepts provided by the US National Incident Management System (NIMS) and the Incident Command System (ICS), specifically through the development of Situational Reports (SitReps), Incident Action Plans (IAP) and Job Action Sheets as methods to implement the framework and concepts discussed. Participants will be led through a series of scenario-based discussions to allow application of critical decision making factors to their organization. At the conclusion of the session, participants will be able to identify next steps for enhancing the synchronization of critical decision making and information analysis within their organizations.

Career Decision Making Based on Graduate's Information Analysis Model

2021 ◽  
Author(s):  
QuanQiu Jia ◽  
LongRi Wen ◽  
JiMin Liu ◽  
ChuangSen Xie ◽  
MingHao Song
Keyword(s):  
Decision Making ◽  
Career Decision ◽  
Career Decision Making ◽  
Information Analysis ◽  
Analysis Model

Decision making with texts: Information analysis and schema acquisition.

1981 ◽  
Vol 110 (3) ◽  
pp. 363-380 ◽  
Author(s):  
Ely Kozminsky ◽  
Walter Kintsch ◽  
Lyle E. Bourne
Keyword(s):  
Decision Making ◽  
Information Analysis ◽  
Schema Acquisition

Chance Information Analysis of a Residential Building Development Case

2013 ◽  
Vol 694-697 ◽  
pp. 2530-2534
Author(s):  
Xiao Hong Xie ◽  
Yong Li
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Age ◽  
Residential Building ◽  
Data Mining Algorithm ◽  
Information Analysis ◽  
Mining Algorithm ◽  
Human Decision

In todays information age, information is wealth, especially the rare but significant chance information for human decision making. And the significant chance information is usually gained by analyzing the scenario graph which is generated by processing the data with a data mining algorithm. So the data mining algorithm is critical to create scenario graph and to obtain valuable information from mass of data. IdeaGraph is a data mining algorithm that not only works on discovering rare and significant chances, but also focuses on uncovering latent relationship among them, it can generate a rich scenario graph for humans comprehension, interpretation and innovation. In this paper, an experiment about residential building development case is performed to evaluate the effect of IdeaGraph in this field.

scholarly journals How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

2021 ◽  
Vol 28 (1) ◽  
pp. e100301
Author(s):  
David Lyell ◽  
Enrico Coiera ◽  
Jessica Chen ◽  
Parina Shah ◽  
Farah Magrabi
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Information Processing ◽  
Ejection Fraction ◽  
Medical Devices ◽  
Human Information Processing ◽  
Information Analysis ◽  
Input And Output ◽  
Descriptive Information ◽  
The Us

ObjectiveTo examine how and to what extent medical devices using machine learning (ML) support clinician decision making.MethodsWe searched for medical devices that were (1) approved by the US Food and Drug Administration (FDA) up till February 2020; (2) intended for use by clinicians; (3) in clinical tasks or decisions and (4) used ML. Descriptive information about the clinical task, device task, device input and output, and ML method were extracted. The stage of human information processing automated by ML-based devices and level of autonomy were assessed.ResultsOf 137 candidates, 59 FDA approvals for 49 unique devices were included. Most approvals (n=51) were since 2018. Devices commonly assisted with diagnostic (n=35) and triage (n=10) tasks. Twenty-three devices were assistive, providing decision support but left clinicians to make important decisions including diagnosis. Twelve automated the provision of information (autonomous information), such as quantification of heart ejection fraction, while 14 automatically provided task decisions like triaging the reading of scans according to suspected findings of stroke (autonomous decisions). Stages of human information processing most automated by devices were information analysis, (n=14) providing information as an input into clinician decision making, and decision selection (n=29), where devices provide a decision.ConclusionLeveraging the benefits of ML algorithms to support clinicians while mitigating risks, requires a solid relationship between clinician and ML-based devices. Such relationships must be carefully designed, considering how algorithms are embedded in devices, the tasks supported, information provided and clinicians’ interactions with them.

scholarly journals Information, Analysis Ximmingning Four Neurons Chemical Name of the Information in Intellectual Evidence

2019 ◽  
Vol 8 (11) ◽  
pp. 3382-3384
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Parallel Processing ◽  
Mathematical Formulation ◽  
Information Analysis ◽  
Experimental Database

The purpose of the research is to extract images from the experimental database by computing the aggregate indicators and introduce them in the parallel processing of models. At present, we have been trying to explain the process of decision-making in algebra of widespread errors and counterfeiting nets in the Ximming network. When deciding whether algorithms in these models are heterogeneous, it does not allow for adequate mathematical formulation of neural networks' transparency problem. As a result, isolation of the images depends on the specialist in neurosurgeons and, in principle, the recommendation.

scholarly journals Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Simin Chen ◽  
Ieong Sanweng
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Industrial Development ◽  
Information Analysis ◽  
Running Speed ◽  
Economic Information ◽  
Internet Age ◽  
Economic Decision Making ◽  
Service Role ◽  
Industrial Economic

In the Internet age, data information is expanding at an alarming rate, and at the same time, it is showing a trend of diversification and fragmentation. How to make reasonable use of this intricate information and effectively use it in the work to give full play to the value of big data (BD) seems urgent. Based on this, this paper studies the economic information analysis and decision-making system under the background of BD. This article first analyzes the development status of our country’s industrial economic decision-making technology, then studies the service role of BD on macroeconomic decision-making in industrial development, then conducts an in-depth study of industrial economic information analysis methods and trends, and designs an industrial economic information model. Finally, this article tested the running speed and interactive function of the system. The test results show that the running speed of the system is very fast, and the interactive function is very accurate.

scholarly journals Information Analysis Support for Decision-Making in Scientific and Technological Development

2020 ◽  
Vol 11 (6) ◽  
pp. 1125
Author(s):  
Boris Onykiy ◽  
Evgeniy Antonov ◽  
Alexey Artamonov ◽  
Evgeny Tretyakov
Keyword(s):  
Decision Making ◽  
Technological Development ◽  
Information Analysis ◽  
Scientific And Technological Development
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