Reference Hub11
Developing Concept Enriched Models for Big Data Processing Within the Medical Domain

Developing Concept Enriched Models for Big Data Processing Within the Medical Domain

Akhil Gudivada, James Philips, Nasseh Tabrizi
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 17
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781799806110|DOI: 10.4018/IJSSCI.2020070105
Cite Article Cite Article

MLA

Gudivada, Akhil, et al. "Developing Concept Enriched Models for Big Data Processing Within the Medical Domain." IJSSCI vol.12, no.3 2020: pp.55-71. http://doi.org/10.4018/IJSSCI.2020070105

APA

Gudivada, A., Philips, J., & Tabrizi, N. (2020). Developing Concept Enriched Models for Big Data Processing Within the Medical Domain. International Journal of Software Science and Computational Intelligence (IJSSCI), 12(3), 55-71. http://doi.org/10.4018/IJSSCI.2020070105

Chicago

Gudivada, Akhil, James Philips, and Nasseh Tabrizi. "Developing Concept Enriched Models for Big Data Processing Within the Medical Domain," International Journal of Software Science and Computational Intelligence (IJSSCI) 12, no.3: 55-71. http://doi.org/10.4018/IJSSCI.2020070105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Within the past few years, the medical domain has endeavored to incorporate artificial intelligence, including cognitive computing tools, to develop enriched models for processing and synthesizing knowledge from Big Data. Due to the rapid growth in published medical research, the ability of medical practitioners to keep up with research developments has become a persistent challenge. Despite this challenge, using data-driven artificial intelligence to process large amounts of data can overcome this difficulty. This research summarizes cognitive computing methodologies and applications utilized in the medical domain. Likewise, this research describes the development process for a novel, concept-enriched model using the IBM Watson service and a publicly available diabetes dataset and knowledge-base. Finally, reflection is offered on the strengths and limitations of the model and enhancements for future experiments. This work thus provides an initial framework for those interested in effectively developing, maintaining, and using cognitive models to enhance the quality of healthcare.