Abstract
BackgroundChronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often “hidden” within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients.MethodsA corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents.ResultsWhen evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors.ConclusionsWe describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0004-6) contains supplementary material, which is available to authorized users.
Highlights
Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare
We describe our strategies for collecting documents for the corpus and our proposed annotation scheme
A simple yet expressive annotation scheme To capture and represent phenotypic information, we developed a typology of clinical concepts (Table 1) taking inspiration from the definition of COPD phenotypes previously proposed [2], i.e., “a single or combination of disease attributes that describe differences between individuals with COPD as they relate to clinically meaningful outcomes.”
Summary
Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. An umbrella term for a range of lung abnormalities, chronic obstructive pulmonary disease (COPD) pertains to medical conditions in which airflow from the lungs is repeatedly impeded. This life-threatening disease, known to be primarily caused by tobacco smoke, is not completely. In order to expedite the process of identifying a given patient’s COPD group, the phenotypic information locked away within these records needs to be automatically extracted and distilled for the clinicians’ perusal
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