Back to Search
Start Over
Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke.
- Source :
-
BMC Medical Informatics & Decision Making . 6/15/2021, Vol. 21 Issue 1, p1-9. 9p. - Publication Year :
- 2021
-
Abstract
- <bold>Background: </bold>Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping.<bold>Methods: </bold>From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels.<bold>Results: </bold>Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype.<bold>Conclusions: </bold>Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 21
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Medical Informatics & Decision Making
- Publication Type :
- Academic Journal
- Accession number :
- 150933910
- Full Text :
- https://doi.org/10.1186/s12911-021-01556-0