Special Issue "Natural Language Processing in Healthcare and Medical Informatics"
Deadline for manuscript submissions: closed (30 June 2020).
Natural language processing (NLP) can help to revolutionize information management and retrieval in healthcare settings. Conventionally, text mining, information retrieval (IR), terminology, and NLP are used for schema mapping. Within this framework, in this Special Issue, we focus on relevant theoretical frameworks and empirical research findings in NLP for healthcare and medical informatics. Unlike the regular engine, we focus on having complex issues in the healthcare domain, where using knowledge classifications, taxonomies with hierarchical ties between knowledge items, and ontologies at a high cost in human labor and involvement must be addressed.
Since NLP tools reorganize artificial intelligence (AI) techniques to focus on linguistic–conceptual relationships, rather than primarily textual analysis, knowledge integration is crucial, where it can help to translate synonymous terms using data and text mining to complete or correct existing knowledge structures. Out of many techniques and/or tools, we focus on deep learning and high-end machine learning tools for (though not limited to) the following tasks:
- Clinical NLP with deep learning;
- Clinical NLP for health outcomes research;
- NLP to analyze/classify health literacy;
- Clinical text mining/classification (weakly supervised case);
- NLP for Clinical Notes (different pathologies);
- NLP on electronic health records (EHRs);
- NLP in healthcare with speech recognition;
- NLP-driven computer assisted coding;
- NLP for clinical trial matching;
- NLP in clinical decision support;
- NLP in healthcare with imaging techniques;
- NLP and data visualization in healthcare.
Dr. KC Santosh
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