Datadriven Discovery of a Sepsis Patients Severity Prediction in the ICU via Pretraining BiLSTM Networks Sepsis is the thirdhighest mortality disease in intensive care units(ICU) and expensive treatment costs, but the best treatment strategy remains uncertain. In this paper, we proposed a pretraining bidirectional LSTM Networks to predict the Sepsis severity of patients in ICU. Most previous models for severity prediction rely on the multitask recurrent neural networks. In addition, stateoftheart neural models based on attention mechanisms do not fully utilize information of organ systems that may be the most crucial features for severity prediction. To address these issues, we propose an endtoend recurrent neural model which incorporates simultaneously analyses different organ systems and intuitively reflect the condition of the patients in a timely fashion. Specifically, we apply a pretraining technique in our model to combines it with labeled data via multitask learning. Experimental results on the realworld clinical dataset (MIMICIII), one of the most popular sepsis severity prediction tasks, demonstrate that our model outperforms existing stateoftheart models.

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.