scholarly journals Clinician Pre-Implementation Perspectives of a Decision-Support Tool for Prediction of Cardiac Arrhythmia based on Machine Learning: Near-Live Feasibility and Qualitative Study (Preprint)

10.2196/26964 ◽  
2021 ◽  
Author(s):  
Stina Matthiesen ◽  
Søren Zöga Diederichsen ◽  
Mikkel Klitzing Hartmann Hansen ◽  
Christina Villumsen ◽  
Mats Christian Højbjerg Lassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Qualitative Study ◽  
Cardiac Arrhythmia ◽  
Decision Support Tool ◽  
Support Tool

A Machine-Learning Clinical Decision Support Tool for Myocardial Infarction Diagnosis

2021 ◽  
Vol 28 ◽  
pp. S13
Author(s):  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Development and Validation of a Machine-Learning-Based Decision Support Tool for Residency Applicant Screening and Review

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jesse Burk-Rafel ◽  
Ilan Reinstein ◽  
James Feng ◽  
Moosun Brad Kim ◽  
Louis H. Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Tool ◽  
Support Tool ◽  
Applicant Screening ◽  
Development And Validation

Machine learning-based design features decision support tool via customers purchasing data analysis

2020 ◽  
pp. 1063293X2096331
Author(s):  
Jian Zhang ◽  
Xingpeng Chu ◽  
Alessandro Simeone ◽  
Peihua Gu
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Data Analysis ◽  
New Product Development ◽  
Decision Support Tool ◽  
Design Features ◽  
Support Tool ◽  
Sales Data ◽  
Product Features ◽  
Product Sales

Decision-making on design features such as specifications and components is an essential aspect of new product development. Customers product preferences and their variations provide the basis of design features decision. Big data of product sales are an emerging source for the obtaining of customers preferences on product features. In this work, a machine learning-based design features decision support tool is proposed through big sales data analysis. Customers preferred product features and their combinations are predicted based on the sales data. Physical feasibility of the product features combinations is considered for customers preference analysis. Cluster analysis method is proposed to identify common and alternative design of product features. Based on specification/component relationships, design features decisions of product components are carried out by grouping product component into noncritical, common, and alternative components. A case study on electric toy cars was included to illustrate the effectiveness of the proposed method.

scholarly journals A MACHINE LEARNING CLINICAL DECISION SUPPORT TOOL FOR MYOCARDIAL INFARCTION DIAGNOSIS

2021 ◽  
Vol 77 (18) ◽  
pp. 653
Author(s):  
Emily Pellegrini ◽  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Engaging with stakeholders to inform the development of a decision-support tool for the NHS health check programme: qualitative study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lirije Hyseni ◽  
Maria Guzman-Castillo ◽  
Chris Kypridemos ◽  
Brendan Collins ◽  
Ellen Schwaller ◽  
...  
Keyword(s):  
Decision Support ◽  
Qualitative Study ◽  
Decision Support Tool ◽  
Health Check ◽  
Support Tool ◽  
Nhs Health Check
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