<unk>  is a risk score to assess severity of chronic liver disease. The score includes nine variables as effective predictors for 90-day mortality after a discharge from a cirrhosis-related admission. The variables include all Model for End-Stage Liver Disease <unk> 's components, as well as sodium, albumin, total cholesterol, white blood cell count, age, and length of stay.<unk>  was created as a result of a collaboration between Massachusetts General Hospital and IBM. The development of<unk>  was based on using unbiased approach toward discovery of biomarkers. In this approach, a feature selection machine learning algorithm observes a large collection of health records and identifies a small set of variables that could serve as the most efficient predictors for a given medical outcome. An example for a notable feature selection method is lasso (least absolute shrinkage and selection operator). Because total cholesterol and hospital length of stay are typically not uniform factors across different hospitals and may vary in different countries, an additional model that included only seven of the nine variables was evaluated. This yielded a performance close to the one of using all nine variables and resulted in the following associations with increased mortality: INR, creatinine, total bilirubin, sodium, WBC, albumin, and age.
MELD-Plus
BODE index
Ferriman–Gallwey score
SAMe-TT2R2 score
Danis–Weber classification
COMLEX-USA
LCA-Vision
APICA 