| Title |
Data-Driven Decision Support for Radiologists: Re-using the National Lung Screening Trial Dataset for Pulmonary Nodule Management
|
|---|---|
| Published in |
Journal of Digital Imaging, June 2014
|
| DOI | 10.1007/s10278-014-9720-1 |
| Pubmed ID | |
| Authors |
James J. Morrison, Jason Hostetter, Kenneth Wang, Eliot L. Siegel |
| 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Ireland | 1 | 2% |
| Unknown | 47 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 16% |
| Other | 6 | 12% |
| Student > Doctoral Student | 6 | 12% |
| Researcher | 6 | 12% |
| Student > Bachelor | 4 | 8% |
| Other | 10 | 20% |
| Unknown | 10 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 34% |
| Mathematics | 3 | 6% |
| Computer Science | 3 | 6% |
| Engineering | 3 | 6% |
| Psychology | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 16 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 October 2015.
All research outputs
#2,559,716
of 22,765,347 outputs
Outputs from Journal of Digital Imaging
#74
of 1,049 outputs
Outputs of similar age
#26,792
of 227,907 outputs
Outputs of similar age from Journal of Digital Imaging
#2
of 17 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,049 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 92% of its peers.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.