Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Area of Science:

• Medical informatics
• Neurology
• Radiology

Background:

• Routinely collected coded health data requires better phenotyping for research and health improvement.
• Current coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) has low precision (<50%).

Purpose of the Study:

• To investigate the feasibility and added value of automated methods using clinical radiology reports to improve stroke subtyping.
• To enhance the accuracy of stroke subtyping beyond existing coded data limitations.

Main Methods:

• Utilized natural language processing and clinical knowledge inference on brain scan reports from UK Biobank participants.
• Assigned stroke subtypes (ischemic, ICH, SAH) and assessed performance using precision and recall at entity and patient levels.

Main Results:

• Automated methods achieved high patient-level precision and recall for ICH (89%) and SAH (82%).
• Performance for ischemic stroke was lower (73% precision, 64% recall), indicating coded data may be preferred for this subtype.
• Entity-level precision and recall ranged from 78% to 100%.

Conclusions:

• Automated analysis of radiology reports offers a feasible, scalable, and accurate solution for improving disease subtyping.
• This method, when combined with administrative coded health data, enhances phenotyping for research and health improvement.
• Further validation in diverse populations is recommended.