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

ABSTRACT:

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

SUBMITTER: Rannikmae K 

PROVIDER: S-EPMC8204419 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature