![]() Massachusetts General Hospital (Snapshot of Science). Harvard, IBM researchers develop prediction model for cirrhosis outcomes. IBM taps machine learning to predict cirrhosis mortality rates. Harvard researchers develop predictive model for cirrhosis outcomes. Developing a new score: how machine learning improves risk prediction. Ĭalculators capable of calculating MELD and MELD-Na are available. Calculators Ī calculator capable of comparing MELD, MELD-Na, and MELD-Plus is available. An example for a notable feature selection method is lasso (least absolute shrinkage and selection operator). 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. The development of MELD-Plus was based on using unbiased approach toward discovery of biomarkers. 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. The variables include all Model for End-Stage Liver Disease (MELD)'s components, as well as sodium, albumin, total cholesterol, white blood cell count, age, and length of stay.īecause 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. The score includes nine variables as effective predictors for 90-day mortality after a discharge from a cirrhosis-related admission. MELD-Plus is a risk score to assess severity of chronic liver disease that was resulted from a collaboration between Massachusetts General Hospital and IBM. Uri Kartoun presenting MELD-Plus at Princeton University, November 2018 ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |