Application of Positive Psychology Education Based on CiteSpace Visual Analysis Tool

2021 ◽  
Author(s):  
Lu Yang
2006 ◽  
Author(s):  
Sergio Flores ◽  
Carolyn Barnes ◽  
Daniel Bunker ◽  
Ren Enriquez ◽  
Kristen Moltzner

2019 ◽  
Vol 10 (02) ◽  
pp. 278-285 ◽  
Author(s):  
Jen Rogers ◽  
Nicholas Spina ◽  
Ashley Neese ◽  
Rachel Hess ◽  
Darrel Brodke ◽  
...  

Objective Visual cohort analysis utilizing electronic health record data has become an important tool in clinical assessment of patient outcomes. In this article, we introduce Composer, a visual analysis tool for orthopedic surgeons to compare changes in physical functions of a patient cohort following various spinal procedures. The goal of our project is to help researchers analyze outcomes of procedures and facilitate informed decision-making about treatment options between patient and clinician. Methods In collaboration with orthopedic surgeons and researchers, we defined domain-specific user requirements to inform the design. We developed the tool in an iterative process with our collaborators to develop and refine functionality. With Composer, analysts can dynamically define a patient cohort using demographic information, clinical parameters, and events in patient medical histories and then analyze patient-reported outcome scores for the cohort over time, as well as compare it to other cohorts. Using Composer's current iteration, we provide a usage scenario for use of the tool in a clinical setting. Conclusion We have developed a prototype cohort analysis tool to help clinicians assess patient treatment options by analyzing prior cases with similar characteristics. Although Composer was designed using patient data specific to orthopedic research, we believe the tool is generalizable to other healthcare domains. A long-term goal for Composer is to develop the application into a shared decision-making tool that allows translation of comparison and analysis from a clinician-facing interface into visual representations to communicate treatment options to patients.


Author(s):  
Saddiga Jaber Al-Ghalib ◽  
Shaden Abdul-Hakeem Al-Khalifah ◽  
Afeefah Y. Salim ◽  
Rana Abdulrahman Hafed Dahlawi

2015 ◽  
Vol 9 (Suppl 6) ◽  
pp. S2 ◽  
Author(s):  
Daekyoung Jung ◽  
Bohyoung Kim ◽  
Robert J Freishtat ◽  
Mamta Giri ◽  
Eric Hoffman ◽  
...  

2013 ◽  
Vol 22 (05) ◽  
pp. 1360008 ◽  
Author(s):  
PATRICIA J. CROSSNO ◽  
ANDREW T. WILSON ◽  
TIMOTHY M. SHEAD ◽  
WARREN L. DAVIS ◽  
DANIEL M. DUNLAVY

We present a new approach for analyzing topic models using visual analytics. We have developed TopicView, an application for visually comparing and exploring multiple models of text corpora, as a prototype for this type of analysis tool. TopicView uses multiple linked views to visually analyze conceptual and topical content, document relationships identified by models, and the impact of models on the results of document clustering. As case studies, we examine models created using two standard approaches: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Conceptual content is compared through the combination of (i) a bipartite graph matching LSA concepts with LDA topics based on the cosine similarities of model factors and (ii) a table containing the terms for each LSA concept and LDA topic listed in decreasing order of importance. Document relationships are examined through the combination of (i) side-by-side document similarity graphs, (ii) a table listing the weights for each document's contribution to each concept/topic, and (iii) a full text reader for documents selected in either of the graphs or the table. The impact of LSA and LDA models on document clustering applications is explored through similar means, using proximities between documents and cluster exemplars for graph layout edge weighting and table entries. We demonstrate the utility of TopicView's visual approach to model assessment by comparing LSA and LDA models of several example corpora.


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