Extracting Medical Concepts from Medical Social Media with Clinical NLP Tools: A Qualitative Study
نویسنده
چکیده
Medical social-media provides a rich source of information on diagnoses, treatment and experiences. For its automatic analysis, tools need to be available that are able to process this particular data. Since content and language of medical social-media differs from those of general social media and of clinical document, additional methods are necessary in particular to identify medical concepts and relations among them. In this paper, we analyse the quality of two existing tools for extracting clinical terms from natural language that were originally developed for processing clinical documents (cTakes, MetaMap) by applying them on a real-world set of medical blog postings. The results show that medical concepts that are explicitly mentioned in texts can reliably be extracted by those tools also frommedical social-media data, but the extraction misses relevant information captured in paraphrase or formulated in common language.
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