Two case studies based on large unstructured sets

Aaron Johnson*, Paul Holmes, Lewis Craske, Marcello Trovati, Nik Bessis, Peter Larcombe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter, we shall present two case studies based on large unstructured datasets. The former specifically considers the Patient Health Questionnaire (PHQ-9), which is the most common depression assessment tool, suggesting the severity and type of depression an individual may be suffering from. In particular, we shall assess a method which appears to enhance the current system in place for health professionals when diagnosing depression. This is based on a combination of a computational assessment method, with a mathematical ranking system defined from a large unstructured dataset consisting of abstracts available from PubMed. The latter refers to a probabilistic extraction method introduced in Trovati et al. (IEEE Trans ADD, 2015, submitted). We shall consider three different datasets introduced in Trovati et al. (IEEE Trans ADD, 2015, submitted; Extraction, identification and ranking of network structures from data sets. In: Proceedings of CISIS, Birmingham, pp 331–337, 2014) and Trovati (Int J Distrib Syst Technol, 2015, in press), whose results clearly indicate the reliability and efficiency of this type of approach when addressing large unstructured datasets. This is part of ongoing research aiming to provide a tool to extract, assess and visualise intelligence extracted from large unstructured datasets.

Original languageEnglish
Title of host publicationBig-Data Analytics and Cloud Computing
Subtitle of host publicationTheory, Algorithms and Applications
PublisherSpringer International Publishing Switzerland
Pages111-125
Number of pages15
ISBN (Electronic)9783319253138
ISBN (Print)9783319253114
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • data sets

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