Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application.

Sadie Cook | Emily Vettese | Dilip Soman | Shannon Hyslop | Susan Kuczynski | Brenda Spiegler | Hailey Davis | Nathan Duong | Stacee Ou Wai | Robert Golabek | Patryk Golabek | Adam Antoszek-Rallo | Tal Schechter | L Lee Dupuis | Lillian Sung
BMC medical informatics and decision making | 2019

We developed Supportive care Prioritization, Assessment and Recommendations for Kids (SPARK), a web-based application designed to facilitate symptom screening by children receiving cancer treatments and access to supportive care clinical practice guidelines primarily by healthcare providers. The objective was to describe the initial development and evaluation of SPARK from the perspective of children.

Pubmed ID: 30630480

Research resources used in this publication

None found

Additional research tools detected in this publication

Antibodies used in this publication

None found

Associated grants

None

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


Spark (tool)

RRID:SCR_006207

A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.

View all literature mentions