Using Local Grammar for Entity Extraction from Clinical Reports

Abstract

-Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.

Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

“…On the other hand, we make three contributions in our work. First, we extract information from real clinical reports such as medical entities (Ghoulam et al, 2015b) and semantic relations between them. Second, we save this information as OWL annotations, and enrich it using different websites, for instance, Health Terminology/Ontology Portal (HeTop).…”

“…The same method as in (Ghoulam et al, 2015b) has been used for the extraction. The output of the analysis phase is a set of medical entities (E1, E2 ... En), a set of semantic relations (R1, R2 ... Rm) and a set of terms (T1, T2 ... Tk) that were not recognized as medical entities or semantic relations.…”

“…Each of these steps is discussed in the following:  Medical entity recognition: Doctors express their information needs using medical terms and entities and that is why it is important to recognize them. In our previous work (Ghoulam et al, 2015b) we used a rule-based approach to medical entity recognition. In this step, we used the same approach for the medical entity recognition.…”

“… Synonyms and descendants extraction: the output of the previous step is a set of medical entities. In this step, our system find the synonyms and their descendants (hyponyms) for each medical entity recognized from the external resource which contains medical entities extracted from real clinical reports (Ghoulam et al, 2015b) and their relationships. And it was validated by medical doctors after enriching it using websites.…”

“…In this paper, we used an external resource in OWL for query expansion process in the French medical domain. It contains medical entities and relations between them that were extracted from real clinical reports and improved from the work developed in (Ghoulam et al, 2015b). We used medical entities, their synonyms and the semantic relations between them to expand the user's query.…”