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Conference articles · 2024

Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP

Adib Ahmed Anik, Paramita Basak Upama, Masud Rabbani, Shiyu Tian, Min Sook Park, Sheikh Iqbal Ahamed, Jake Luo, Hyunkyoung Oh

2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) · Pages 862–869

Abstract

This study suggests a way to utilize the existing medical ontology and natural language processing techniques to extract major medical concepts from lay vocabularies of health consumers on social media and group them based on the defined semantic types in the ontology. Diabetes-related discussions on Tumblr was used to test the efficiency of SpaCy and the Markov-Viterbi algorithm to map lay medical terms to the defined medical concepts in the UMLS. The system discussed in this paper can better analyze free texts, take care of word ambiguity and extract the lifestyle indicators from the daily life discussions of diabetic people on Tumblr. The findings of this study can contribute to developing health applications that track the health behavior of those living with chronic conditions such as diabetes. This approach can also assist researchers who are interested in processing lay languages used by health consumers to foster an understanding of their health behavior.

Publication details

Category
Conference articles
Authors & affiliations
  1. Adib Ahmed AnikUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  2. Paramita Basak UpamaUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  3. Masud RabbaniUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  4. Shiyu TianUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  5. Min Sook ParkSchool of Information, College of Communication and Information, Florida State University, FL, USA
  6. Sheikh Iqbal AhamedUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  7. Jake LuoCollege of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
  8. Hyunkyoung OhCollege of Nursing, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
Venue
2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
Publisher
IEEE
Record
Pages 862–869
Published
2024
Cite this work

Adib Ahmed Anik, Paramita Basak Upama, Masud Rabbani, Shiyu Tian, Min Sook Park, Sheikh Iqbal Ahamed, Jake Luo, Hyunkyoung Oh. “Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP”. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 862–869. (2024). https://doi.org/10.1109/COMPSAC61105.2024.00119.

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Research topics

  • Natural Language Processing (NLP)
  • Lifestyle Indicators
  • Diabetes
  • Unified Medical Language System (UMLS)
  • Chronic Diseases
  • UMLS
  • consumer health vocabulary
  • natural language processing
  • diabetes
  • social media
  • medical concept extraction
  • semantic types

Research context

Explore the connected research focus

Mobile-health AI Conversational AI & VoiS