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PHARM - Association Rule Mining for Predictive Health.

Chih-Wen Cheng1, Greg S Martin2, Po-Yen Wu1

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

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|September 3, 2016
PubMed
Summary
This summary is machine-generated.

Predictive health systems can now offer objective health assessments. This study developed PHARM, a system using data mining to predict mental illness from psychological factors, aiding preventative care.

Keywords:
Decision support systemassociation rule mininghealth modeling and predictionpredictive health

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Area of Science:

  • Healthcare Informatics
  • Data Mining
  • Predictive Analytics

Background:

  • Predictive health focuses on maintaining wellness over treating illness.
  • Computer-based decision support systems enhance quantitative health assessment for objective advice.
  • Data mining for predictive health presents unique challenges, limiting current system development.

Purpose of the Study:

  • To research and develop an interactive decision support system named PHARM.
  • To integrate association rule mining for objective health assessment and prediction.
  • To collaborate with the Emory Center for Health Discovery and Well Being (CHDWB®).

Main Methods:

  • Utilized association rule mining techniques.
  • Developed an interactive decision support system (PHARM).
  • Conducted a case study involving psychological factors and mental illness prediction.

Main Results:

  • Generated 12 quantitative and objective rules for health prediction.
  • Successfully predicted mental illness based on five psychological factors.
  • Demonstrated the system's value in preventing potential illness.

Conclusions:

  • The PHARM system offers valuable, usable decision support for predictive health.
  • The system enables prioritization of advice and action plans for disease risk reduction.
  • Highlights the potential of data mining in advancing proactive healthcare models.