Learning Healthcare System enabled by Real-time Knowledge Extraction from Text data

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Learning Healthcare System enabled by Real-time Knowledge Extraction from Text data

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Kaggal, Vinod

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2019-07

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Thesis or Dissertation

Abstract

We have a critical void in the clinical informatics ecosystems in enabling information captured in the Electronic Health Record (EHR) to be transformed into actionable knowledge. Incorporating knowledge into clinical practice leveraging informatics based analytical tools is critical in delivering optimal clinical care and lead us toward an effective Learning Healthcare System (LHS). A robust infrastructure plays a very critical role in enabling such clinical informatics ecosystems. This robust infrastructure must guarantee the ability to manage data volume and velocity, variety and veracity. This thesis work accomplishes i) Proposal of a data model to support building a robust analytics framework to automatically compute the knowledge within the EHR ii) Infrastructure to scale-up analytics and knowledge delivery iii) Clinical and Research projects that utilize this infrastructure for near real-time analysis of text data to derive intuitive clinical inferences of patient’s multi-dimensional data.

Keywords

big data

information extraction

knowledge delivery

natural language processing

nlp

patient care

Description

University of Minnesota M.S. thesis.July 2019. Major: Biomedical Informatics and Computational Biology. Advisors: Hongfang Liu, Yuk Sham. 1 computer file (PDF); vi, 33 pages.

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Kaggal, Vinod. (2019). Learning Healthcare System enabled by Real-time Knowledge Extraction from Text data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206702.

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