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Designing “Living” Evidence Networks for Health Optimisation: Knowledge Extraction of Patient-Relevant Outcomes in Mental Disorders

  • Hoang D. Nguyen
  • Øystein Eiring
  • Danny Chiang Choon Poo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)

Abstract

Over 70 randomised controlled trials (RCTs) are published in MEDLINE every day; in which the volume and velocity of unstructured evidence data have become a great challenge to human manual processing capabilities. There is an emerging need for a dynamic, evolving design of “living” evidence networks as the best source of health optimisation in evidence-based medicine. This study, therefore, investigated the text and layout features of unstructured full-texts in the biomedical literature to design IT artefacts for building high-quality and up-to-date evidence networks of RCTs. As a result, network meta-analyses can be automated for comparative adverse effects of treatments in chronic disorders such as Major Depressive Disorder and Bipolar Disorder. The study outcomes extended the technological boundary of health optimisation technologies, and contributed to the cumulative development of patient-relevant health care and shared decision-making.

Keywords

Evidence networks Living systematic reviews Health optimisation Knowledge extraction Major depressive disorder Bipolar disorder 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hoang D. Nguyen
    • 1
  • Øystein Eiring
    • 2
  • Danny Chiang Choon Poo
    • 1
  1. 1.Department of Information Systems and Analytics, School of ComputingNational University of SingaporeSingaporeSingapore
  2. 2.Norwegian Knowledge Centre for the Health ServicesOsloNorway

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