Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations.

Objective: This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations.

Methods: This retrospective, observational cohort study, used a random 5% sample of 770,777 fee-for-service Medicare beneficiaries with an inpatient hospitalization between 2009-2011. The machine learning algorithms tested included: support vector machine, random forest, multilayer perceptron, extreme gradient boosted tree, and logistic regression.

Abnormal Calcium Levels During Trauma Resuscitation Are Associated With Increased Mortality, Increased Blood Product Use, and Greater Hospital Resource Consumption: A Pilot Investigation.

Background: Admission hypocalcemia predicts both massive transfusion and mortality in severely injured patients. However, the effect of calcium derangements during resuscitation remains unexplored. We hypothesize that any hypocalcemia or hypercalcemia (either primary or from overcorrection) in the first 24 hours after severe injury is associated with increased mortality.