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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders, 4th edn. American Psychiatric Association, Washington, DC
Gelaye B et al (2013) Validity of the patient health questionnaire-9 for depression screening and diagnosis in East Africa. Psychiatry Res Dec 210(2). doi:10.1016/j.psychres.2013.07.015
Harker PT (1986) Incomplete pairwise comparisons in the analytic hierarchy process. University of Pennsylvania, Philadelphia
Kroenke K, Spitzer R (2002) The PHQ-9: a new depression diagnostic and severity measure. Psychiatric Ann 32:1–6
Manea L, Gilbody S, McMillan D (2012) Optimal cut off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ 184(3):E191–E196
Murphy RO Prof (2013) Decision theory: rationality, risk and human decision making
Thomas H (2013) Patient Health Questionnaire (PHQ-9). Patient, Available from http://patient.info/doctor/patient-health-questionnaire-phq-9. Last accessed 17 Jun 2015
Pubmed, Available from http://www.ncbi.nlm.nih.gov/pubmed. [15 April 2014]
PharmGKB, Available from https://www.pharmgkb.org/. [15 April 2014]
Saaty TL et al (1990) How to make a decision: the analytic hierarchy process, Pittsburgh
Salkind N, Rasmussen K (2007) Encyclopedia of measurement and statistics. SAGE, Thousand Oaks. doi:10.4135/9781412952644
Tavakol M, Dennick R (2011) Making sense of Cronbach’s Alpha. Int J Med Educ 2:53–55
Trovati M (2015) Reduced topologically real-world networks: a big-data approach. Int J Distrib Syst Technol (IJDST) 6(2):13–27
Trovati M, Bessis N (2015) An influence assessment method based on co-occurrence for topologically reduced big datasets. Soft computing. Springer, Berlin/Heidelberg
Trovati M, Bessis N, Huber A, Zelenkauskaite A, Asimakopoulou E (2014) Extraction, identification and ranking of network structures from data sets. In: Proceedings of CISIS, Birmingham, pp 331–337
Trovati M, Bessis N, Palmieri F, Hill R (under review) Extracting probabilistic information from unstructured large scale datasets. IEEE Syst J
Vale L, Silcock J, Rawles J (1997) An economic evaluation of thrombolysis in a remote rural community. BMJ 314:570–572
Wing J et al (1990) SCAN schedules for clinical assessment in neuropsychiatry. Arch Gen Psychiatry 47(6):589–593. doi:10.1001/archpsyc.1990.01810180089012
Acknowledgements
This research was partially supported by the University of Derby Undergraduate Research Scholarship Scheme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Johnson, A., Holmes, P., Craske, L., Trovati, M., Bessis, N., Larcombe, P. (2015). Two Case Studies Based on Large Unstructured Sets. In: Trovati, M., Hill, R., Anjum, A., Zhu, S., Liu, L. (eds) Big-Data Analytics and Cloud Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-25313-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-25313-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25311-4
Online ISBN: 978-3-319-25313-8
eBook Packages: Computer ScienceComputer Science (R0)