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Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis

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Abstract

In healthcare services, information extraction is the key to understand any corpus-based knowledge. The process becomes laborious when the annotation is done manually for the availability of a large number of text corpora. Hence, future automated extraction systems will be essential for groups of experts such as doctors and medical practitioners as well as non-experts such as patients, to ensure enhanced clinical decision-making for improving healthcare systems. Such extraction systems can be developed using medical concepts and concept-related features as the part of a structured corpus. The latter can assist in assigning the category and sentiment to each of the medical concepts and their lexical contexts. These categories and sentiment assignments constitute semantic relations of medical concepts, with their context, represented by sentences of the corpus. This paper presents a new domain-based knowledge lexicon coupled with a machine learning approach to extract semantic relations. This is done by assigning category and sentiment of the medical concepts and contexts. The categories considered in this research, are diseases, symptoms, drugs, human_anatomy, and miscellaneous medical terms, whereas sentiments are considered as positive and negative. The proposed assignment systems are developed on the top of WordNet of Medical Event (WME) lexicon. The developed lexicon provides medical concepts and their features, namely Parts-Of-Speech (POS), gloss (descriptive explanation), Similar Sentiment Words (SSW), affinity score, gravity score, polarity score, and sentiment. Several well-known supervised classifiers, including Naïve Bayes, Logistic Regression, and support vector-based Sequential Minimal Optimization (SMO) have been applied to evaluate the developed systems. The proposed approaches have resulted in a concepts clustering application by identifying the semantic relations of concepts. The application provides potential exploitation in several domains, such as medical ontologies and recommendation systems.

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Notes

  1. http://www.nactem.ac.uk/GENIA/tagger/

  2. https://catalog.ldc.upenn.edu/LDC2008T21

  3. http://alt.qcri.org/semeval2015/task6/

  4. http://alexabe.pbworks.com/f/Dictionary+of+Medical+Terms+4th+Ed.-+(Malestrom).pdf http://alexabe.pbworks.com/f/Dictionary+of+Medical+Terms+ 4th+Ed.-+(Malestrom).pdf

  5. http://sentiwordnet.isti.cnr.it/

  6. http://sentic.net/downloads/

  7. https://www.cs.uic.edu/

  8. http://neuro.imm.dtu.dk/wiki/

  9. https://en.wikipedia.org/wiki/Cohen’skappa

  10. http://www.nltk.org/

  11. http://alt.qcri.org/semeval2015/task6/

  12. http://www.medicinenet.com/script/main/hp.asp

  13. http://alt.qcri.org/semeval2015/task6/

  14. http://www.medicinenet.com/script/main/hp.asp

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Anupam Mondal, Dipankar Das & Sivaji Bandyopadhyay

  2. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore

    Erik Cambria

  3. Division of Computing Science and Maths, Faculty of Natural Sciences, University of Stirling, Stirling, Scotland, UK

    Amir Hussain

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  1. Anupam Mondal
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Mondal, A., Cambria, E., Das, D. et al. Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis. Cogn Comput 10, 670–685 (2018). https://doi.org/10.1007/s12559-018-9567-8

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