Paper Info
Title
Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.
Abstract
Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.
Year
DOI
Venue
2015
10.1007/s10278-014-9720-1
J. Digital Imaging
Keywords
Field
DocType
Decision support,Data mining,Decision support techniques,Web technology
Data mining,Population,Lung cancer screening,Computer science,Clinical trial,Artificial intelligence,Clinical decision support system,National Lung Screening Trial,JavaScript,SQL,Computer vision,Information retrieval,Decision support system
Journal
Volume
Issue
ISSN
28
1
1618-727X
Citations 
PageRank 
References 
5
0.93
2
Authors
4
Name
Order
Citations
PageRank
James J Morrison151.61
Jason Hostetter2122.57
Kenneth C. Wang3155.81
Eliot Siegel430280.13