Automated determination of metastases in unstructured radiology reports for eligibility screening in oncology clinical trials.

作者: Valentina I Petkov , Lynne T Penberthy , Bassam A Dahman , Andrew Poklepovic , Chris W Gillam

DOI: 10.1177/1535370213508172

关键词:

摘要: Enrolling adequate numbers of patients that meet protocol eligibility criteria in a timely manner is critical, yet clinical trial accrual continues to be problematic. One approach these challenges utilize technology automatically screen for eligibility. This manuscript reports on the evaluation different automated approaches determine metastatic status from unstructured radiology using Clinical Trials Eligibility Database Integrated System (CTED). The study sample included all (N = 5,523) with radiologic diagnostic studies (N = 10,492) completed two-week period. Eight search algorithms (queries) within CTED were developed and applied reports. performance each algorithm was compared reference standard which consisted physician’s review Sensitivity, specificity, positive, negative predicted values calculated algorithm. number identifie...

参考文章(38)
Rebecca Smith-Bindman, Diana L Miglioretti, David Carrell, Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES). american medical informatics association annual symposium. pp. 889- ,(2007)
Marguerite Swietlik, Louis M. Bell, Robert W. Grundmeier, Research subject enrollment by primary care pediatricians using an electronic health record. american medical informatics association annual symposium. ,vol. 2007, pp. 289- 293 ,(2007)
Tomas Borda, Chunhua Weng, Adam B. Wilcox, Karina W. Davidson, Nicole Gray Weiskopf, Candido Batres, John T. Bigger, A Real-Time Screening Alert Improves Patient Recruitment Efficiency american medical informatics association annual symposium. ,vol. 2011, pp. 1489- 1498 ,(2011) , 10.7916/D8BK1CG6
Brigitte Séroussi, Jacques Bouaud, Eric-Charles Antoine, Laurent Zelek, Marc Spielmann, Using ONCODOC as a Computer-Based Eligibility Screening System to Improve Accrual onto Breast Cancer Clinical Trials artificial intelligence in medicine in europe. pp. 421- 430 ,(2001) , 10.1007/3-540-48229-6_58
Stéphane M. Meystre, Joyce A. Mitchell, Vikrant G. Deshmukh, A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations. american medical informatics association annual symposium. ,vol. 2009, pp. 442- 446 ,(2009)
George Hripcsak, Gilad J Kuperman, Carol Friedman, None, Extracting findings from narrative reports : Software transferability and sources of physician disagreement Methods of Information in Medicine. ,vol. 37, pp. 1- 7 ,(1998) , 10.1055/S-0038-1634499
Peter M. Ellis, Phyllis N. Butow, Martin H.N. Tattersall, Stewart M. Dunn, Nehmat Houssami, Randomized Clinical Trials in Oncology: Understanding and Attitudes Predict Willingness to Participate Journal of Clinical Oncology. ,vol. 19, pp. 3554- 3561 ,(2001) , 10.1200/JCO.2001.19.15.3554
I. A. McCowan, D. C. Moore, A. N. Nguyen, R. V. Bowman, B. E. Clarke, E. E. Duhig, M.-J. Fry, Collection of Cancer Stage Data by Classifying Free-text Medical Reports Journal of the American Medical Informatics Association. ,vol. 14, pp. 736- 745 ,(2007) , 10.1197/JAMIA.M2130
Paul Hanbury, Will Bridewell, Gregory F. Cooper, Wendy W. Chapman, Bruce G. Buchanan, Evaluation of negation phrases in narrative clinical reports. american medical informatics association annual symposium. pp. 105- 109 ,(2001)