Your browser doesn't support javascript.
loading
Identification of Incidental Pulmonary Nodules in Free-text Radiology Reports: An Initial Investigation.
Oliveira, Lucas; Tellis, Ranjith; Qian, Yuechen; Trovato, Karen; Mankovich, Gabe.
Affiliation
  • Oliveira L; Clinical Informatics Solutions and Service, Philips Research North America, NY, USA.
  • Tellis R; Clinical Informatics Solutions and Service, Philips Research North America, NY, USA.
  • Qian Y; Clinical Informatics Solutions and Service, Philips Research North America, NY, USA.
  • Trovato K; Clinical Informatics Solutions and Service, Philips Research North America, NY, USA.
  • Mankovich G; Clinical Informatics Solutions and Service, Philips Research North America, NY, USA.
Stud Health Technol Inform ; 216: 1027, 2015.
Article in En | MEDLINE | ID: mdl-26262327
ABSTRACT
Advances in image quality produced by computed tomography (CT) and the growth in the number of image studies currently performed has made the management of incidental pulmonary nodules (IPNs) a challenging task. This research aims to identify IPNs in radiology reports of chest and abdominal CT by Natural Language Processing techiniques to recognize IPN in sentences of radiology reports. Our preliminary analysis indicates vastly different pulmonary incidental findings rates for two different patient groups.
Subject(s)
Search on Google
Add to My VHL
Print
XML
PubMed Links

Collection: 01-internacional Database: MEDLINE Main subject: Natural Language Processing / Radiography, Abdominal / Diagnosis, Computer-Assisted / Radiology Information Systems / Decision Support Systems, Clinical / Machine Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2015 Document type: Article Affiliation country: United States
Search on Google
Add to My VHL
Print
XML
PubMed Links

Collection: 01-internacional Database: MEDLINE Main subject: Natural Language Processing / Radiography, Abdominal / Diagnosis, Computer-Assisted / Radiology Information Systems / Decision Support Systems, Clinical / Machine Learning Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2015 Document type: Article Affiliation country: United States