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Table 1 Summary variables used to train machine learning algorithms

From: Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation and machine learning study

Variable nameDescriptionMeanMedianMaxMinSD
Abs.diffAbsolute difference first-last reported drink−0.39012−173.14
Avg.drinksAverage reported drinks3.2631502.42
EntriesTotal number of entries10.9784819.73
interceptIntercept of trajectory0.050.224.79−2.731.13
IQR.drinksInter-quartile range of drinks1.69113.501.89
Max.drinksMaximum reported drinks6.0562203.98
Median.drinksMedian reported drinks2.9231502.62
Min.drinksMinimum reported drinks1.6711502.24
n.bingeNumber of binge drinking entries0.7202001.71
n.heavyNumber of heavy drinking entries2.3513503.32
n.lightNumber of light drinking entries7.944808.79
Perc.bingePercentage binge drinking entries0.090100.2
Perc.heavyPercentage of heavy drinking entries0.260.17100.3
Perc.lightPercentage of light drinking entries0.650.75100.35
Range.drinksRange of reported drinks4.3942204.15
Rel.diffRelative difference first-last reported drinks−0.0602.5−4.50.61
slopeSlope of trajectory−0.01−0.019.21−142.34
Sum.drinksTotal sum of reported drinks28.2916208032.23
  1. 1Bing-drinking defined as > 6 for women, > 8 for men
  2. 2Heavy drinking defined as > 3 and < 7 for women, > 4 and < 9 for men
  3. 3Light drinking defined as < 4 drinks for women, < 5 for men