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 An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3535 ◽  
Author(s):  
Wang ◽  
Li ◽  
Zhang ◽  
Zou
Keyword(s):  
Decision Making ◽  
Liquid Steel ◽  
Classification Problem ◽  
Rule Extraction ◽  
Black Box ◽  
Production Data ◽  
Integrated Methodology ◽  
Irrelevant Attributes ◽  
Learning Machine ◽  
Elm Classifier

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

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.

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