Name
Abstract | ||
|---|---|---|
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging examinations. In this paper, we present a text processing pipeline to automatically identify clinically important recommendation sentences in radiology reports. Our extraction pipeline is based on natural language processing (NLP) and supervised text classification methods. To develop and test the pipeline, we created a corpus of 800 radiology reports double annotated for recommendation sentences by a radiologist and an internist. We ran several experiments to measure the impact of different feature types and the data imbalance between positive and negative recommendation sentences. Our fully statistical approach achieved the best f-score 0.758 in identifying the critical recommendation sentences in radiology reports. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jbi.2012.12.005 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
important recommendation sentence,imaging examination,recommendation sentence,critical recommendation sentence,negative recommendation sentence,follow-up recommendation,radiology report,text processing pipeline,track radiology recommendation,extraction pipeline,natural language processing | Data mining,Information retrieval,Computer science,Radiology information systems,Data imbalance,Radiology,Health informatics,Text processing | Journal |
Volume | Issue | ISSN |
46 | 2 | 1532-0480 |
Citations | PageRank | References |
16 | 1.26 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meliha Yetisgen-Yildiz | 1 | 328 | 34.25 |
| Martin L Gunn | 2 | 26 | 2.66 |
| Fei Xia | 3 | 16 | 1.26 |
| Thomas H. Payne | 4 | 49 | 14.26 |