Abstract

Electronic health records (EHRs) contain information critical to the realisation of the promise of personalised medicine, but also data essential for the discovery of the molecular basis of disease. Clinical information systems and EHRs were not developed for the discovery, integration and export of information, most being based on the concept of paper records going back to the 1990s. Consequently we find in EHRs information contained in administrative, diagnostic and procedure codes, which are highly structured and standardised, the results of investigative tests, ranging from blood chemistry to images, which might be regarded as partially structured information, and finally narrative reports of clinical encounters and discharge letters which are rich sources of information but completely unstructured. Reliably extracting and integrating these types of information is a huge challenge, but the ability to retrieve coded and quantitative data into a common symbolic framework opens up the possibility of connecting these data together with the large amounts of background knowledge now available.

I will discuss three approaches to extracting and using EHR information: the first uses the Komenti platform which is designed to extract information from free text into semantically formalised ontological annotations, the second is an approach to combine quantitative data into that same semantic framework. The third, a new resource, axiomatises ICD -10 terms uses the Human phenotype ontology for integration with existing knowledge and, for example, patient classification. The promise of these orthogonal approaches will be discussed.