Using Machine Learning to Predict Hyperchloremia in Critically Ill Patients Elevated serum chloride levels (hyperchloremia) and the administration of intravenous (IV) fluids with high chloride content have both been associated with increased morbidity and mortality in certain subgroups of critically ill patients, such as those with sepsis. Here, we demonstrate this association in a general intensive care unit (ICU) population using data from the Medical Information Mart for Intensive Care III (MIMICIII) database and propose the use of supervised learning to predict hyperchloremia in critically ill patients. Clinical variables from records of the first 24h of adult ICU stays were represented as features for four predictive supervised learning classifiers. The best performing model was able to predict secondday hyperchloremia with an AUC of 0.80 and a ratio of 5 false alerts for every true alert, which is a clinicallyactionable rate. Our results suggest that clinicians can be effectively alerted to patients at risk of developing hyperchloremia, providing an opportunity to mitigate this risk and potentially improve outcomes.

OneYear Mortality Prediction in ICU Patients with Diagnosis of Sepsis Driven by Population Similarities Traditional Intensive Care Unit (ICU) outcome prediction models are based on the analysis of large populations, and often provide statistically rigorous results for an average patient but are also expensive, timeconsuming, and prone to selection bias; moreover, these indicators usually lack the precision required for use at the individual level, since they present significant errors at patient data away from the average. In this work we present an evaluation of the impact and relevance of three different patient similarity metrics on the oneyear mortality prediction when patients are related by the same diagnosis, sepsis. We use data of 16.000 admissions of patients with sepsis from the Medical Information Mart for Intensive Care (MIMICIII) database.