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

Akhil Gudivada, James Philips, Nasseh Tabrizi
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引用次数: 8

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.
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发展医学领域大数据处理的概念丰富模型
在过去的几年中,医疗领域一直在努力将人工智能(包括认知计算工具)纳入其中,以开发用于处理和综合大数据知识的丰富模型。由于发表的医学研究的快速增长,医生跟上研究发展的能力已经成为一个持续的挑战。尽管存在这一挑战,但使用数据驱动的人工智能来处理大量数据可以克服这一困难。本研究总结了认知计算方法及其在医学领域的应用。同样,本研究描述了使用IBM Watson服务和公开可用的糖尿病数据集和知识库的新颖、概念丰富的模型的开发过程。最后,对模型的优点和局限性进行了反思,并对未来的实验进行了改进。因此,这项工作为那些对有效开发、维护和使用认知模型以提高医疗保健质量感兴趣的人提供了一个初步框架。
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