University of Illinois Urbana-Champaign

Natural language processing to support evidence quality assessment of clinical literature

Hoang, Khanh Linh

Content Files
HOANG-DISSERTATION-2023.pdf

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https://hdl.handle.net/2142/120224

Description

Title
Natural language processing to support evidence quality assessment of clinical literature
Author(s)
Hoang, Khanh Linh
Issue Date
2023-04-03
Director of Research (if dissertation) or Advisor (if thesis)
Kilicoglu, Halil
Doctoral Committee Chair(s)
Kilicoglu, Halil
Committee Member(s)
  • Ludäscher, Bertram
  • Diesner, Jana
  • Boyce, Richard D
Department of Study
Information Sciences
Discipline
Library & Information Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Natural Language Processing
  • Machine Learning
  • Biomedical
  • Evidence Quality Assessment
Abstract
Evidence Synthesis is the process of synthesizing information from clinical literature to translate the research findings into patient care and healthcare policy. Throughout the evidence synthesis process, a critical yet challenging step is the quality assessment of clinical studies. Quality in research can be considered through two aspects: methodological quality which concerns how rigorously a research is designed and conducted, and reporting quality which describes how transparently a piece of scientific work is reported as a publication. This thesis explores natural language processing (NLP) approaches to support evidence quality assessment of clinical studies. Specifically, I consider different levels of information granularity used for evidence assessment, and implemented three machine learning developments: (1) Classification of evidence types from clinical publications based on study designs, (2) Classification of sentences from randomized controlled trials (RCTs) with checklist items recommended in reporting guidelines, (3) Extraction of fine- grained methodological characteristics from RCTs to assist methodological quality assessment. Applications of these NLP approaches range from assisting authors in checking their manuscripts for compliance with reporting guidelines and supporting journal editors and peer reviewers in assessing papers (pre-publication) to assisting systematic reviewers in synthesizing evidence and meta-researchers in studying research rigor and transparency (post-publication).
Graduation Semester
2023-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/120224
Copyright and License Information
N/A

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