Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.
J Digit Imaging
; 28(1): 18-23, 2015 Feb.
Article
em En
| MEDLINE
| ID: mdl-24965276
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Radiologia
/
Programas de Rastreamento
/
Técnicas de Apoio para a Decisão
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Tomografia Computadorizada Espiral
/
Mineração de Dados
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article