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Optimal information networks: Application for data-driven integrated health in populations
Joseph L Servadio1, Matteo Convertino2,3,4
1Division of Environmental Health Sciences, HumNat Lab, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA.
Science Advances
|February 10, 2018
Summary
This study introduces maximum entropy networks (MENets) to create data-driven composite health indicators. This novel approach overcomes limitations of traditional methods, enabling better population health comparisons.
Area of Science:
- Complex Systems Science
- Public Health Informatics
- Network Science
Background:
- Composite health indicators often rely on assumptions, not data.
- Existing methods neglect variable relatedness and redundancy.
- A unified approach for population health assessment is missing.
Purpose of the Study:
- To develop a data-driven method for composite health indicators.
- To address limitations in traditional variable selection for health indicators.
- To enable systematic comparison of integrated health statuses across populations.
Main Methods:
- Utilized maximum entropy networks (MENets) for variable interrelatedness assessment.
- Employed transfer entropy to analyze variable relationships.
- Defined optimal information networks (OINs) as scale-invariant MENets for decision-making.
Main Results:
- Developed a novel systemic health indicator using health outcome data from US cities.
- Demonstrated the capability of MENets to capture complex variable interdependencies.
- Showcased OINs for informed decision-making in health assessment.
Conclusions:
- MENets offer a model-free, evidence-based approach to composite indicator development.
- The proposed method provides a unified framework for assessing integrated population health.
- This approach facilitates systematic and objective comparisons of health statuses across different populations.
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