Named Entity Recognition over Electronic Health Records Through a Combined Dictionary-based Approach

In health care information systems, electronic health records are an important part of the knowledge concerning individual health histories. Extracting valuable knowledge from these records represents a challenging task because they are composed of data of different kind: images, test results, narrative texts that include both highly codified and a variety of notes which are diverse in language and detail, as well as ad hoc terminology, including acronyms and jargon, far from being highly codified. This paper proposes a combined approach for the recognition of named entities in such narrative texts. This approach is a composition of three different methods. The possible combinations are evaluated and the resulting composition shows an improvement of the recall and a limited impact on precision for the named entity recognition process.