Journal article
Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke
- Abstract:
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Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1186/s12911-021-01556-0
Authors
- Funder identifier:
- https://ror.org/03x94j517
- Grant:
- MC_PC_18029
- Publisher:
- BioMed Central
- Journal:
- BMC Medical Informatics and Decision Making More from this journal
- Volume:
- 21
- Issue:
- 1
- Article number:
- 191
- Publication date:
- 2021-06-15
- Acceptance date:
- 2021-06-08
- DOI:
- EISSN:
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1472-6947
- Pmid:
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34130677
- Language:
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English
- Keywords:
- Pubs id:
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1302407
- Local pid:
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pubs:1302407
- Deposit date:
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2025-02-03
- ARK identifier:
Terms of use
- Copyright holder:
- Rannikmäe et al.
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
- Licence:
- CC Attribution (CC BY)
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