Smart stats make use of large-scale health insurance claims

Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes

We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (varSES = 0.002), daily air quality (varAQI = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.

READ MORE …

More machine learning making models...

Sberbank creates algorithm to do data scientists' job

Sberbank creates algorithm to do data scientists' job - More machine learning making models...

It seems that even data scientists are not immune to the corrosive impact of artificial intelligence on the jobs market. Russia's Sberbank claims to have created an algorithm - Auto ML (machine learning) - that "acts like a data scientist", creating its own models that then solve application tasks.

The bank carried out its first pilot in January, using Auto ML algos to create several baseline models to help with the targeting of sales campaigns.

READ MORE…