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From Novice to Expert Narratives of Dermatological Disease

  • Nse Obot
  • , Laura Ormalley
  • , Ifeoma Nwogu
  • , Qi Yu
  • , Wei Shi Shi
  • , Xuan Guo
  • SUNY Buffalo
  • Washington & Jefferson College
  • Rochester Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Medical diagnosis requires extensive knowledge of pathological characteristics and the clinical training necessary to master specific domains. In the domain of dermatology, these properties include the size, shape, distribution, color, and location of symptoms. We have built and trained a tag recommender with expert narratives of dermatological disease, then explored the effectiveness of this system on novice narratives. The system mitigates the prerequisite of domain knowledge, empowering a novice to enhance their medical descriptions or even reach an accurate diagnosis. After collecting novice narratives, we explored word alignment to provide a mapping between expert and novice vocabulary allowing novice input to be augmented with expert terminology. Ultimately, we found that our system is an effective educational tool, which could be improved by word alignment and other techniques.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages131-136
Number of pages6
ISBN (Electronic)9781538632277
DOIs
StatePublished - Oct 2 2018
Event16th IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 - Athens, Greece
Duration: Mar 19 2018Mar 23 2018

Publication series

Name2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018

Conference

Conference16th IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
Country/TerritoryGreece
CityAthens
Period03/19/1803/23/18

Keywords

  • Dermatology
  • Expert systems
  • Graphical user interfaces
  • Medical diagnosis
  • Natural language processing
  • Recommender systems
  • Semantics
  • Support vector machines

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