Moving from descriptive to causal analytics: Case study of discovering knowledge from us health indicators warehouse

Jack Schryver, Mallikarjun Shankar, Songhua Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The knowledge management community has introduced a multitude of methods for knowledge discovery on large datasets. In the context of public health intelligence, we integrated and incorporated some of these methods into an analyst's workflow that proceeds from the data-centric descriptive level of analysis to the model-centric causal level of reasoning. We show several case studies of the proposed analyst's workflow as applied to the US Health Indicators Warehouse (HIW), which is a medium scale, public dataset regarding community health information as collected by the US federal government. In our case studies, we demonstrate a series of visual analytics efforts targeted at the HIW, including visual analysis according to correlation matrices, multivariate outlier analysis, multiple linear regression of Medicare costs, confirmatory factor analysis, and hybrid scatterplot and heatmap visualization for distributions of a group of health indicators. We conclude by sketching a preliminary framework for examining causal dependence hypotheses for future data science research in public health.

Original languageEnglish (US)
Title of host publicationSHB'12 - Proceedings of the 2012 ACM International Workshop on Smart Health and Wellbeing, Co-located with CIKM 2012
Pages1-8
Number of pages8
DOIs
StatePublished - Dec 7 2012
Event2012 ACM International Workshop on Smart Health and Wellbeing, SHB 2012 - Co-located with CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Oct 29 2012

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other2012 ACM International Workshop on Smart Health and Wellbeing, SHB 2012 - Co-located with CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1210/29/12

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Keywords

  • Community health indicators
  • Machine learning
  • Multivariate statistics
  • Visual analytics

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