Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups.
other · Level V
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- Record sourced from PubMed, PMID 33362207.
- Also identified by DOI 10.1371/journal.pone.0243229 and PMC identifier 7757823.
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Abstract
Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012-2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses.
Medical subject headings
- Big Data
- Medical Informatics