Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning.
In order to evaluate the effect of hydrocortisone on apoptosis in the jejunum of horses subjected to ischemia and reperfusion, ten horses were paired and grouped into two groups -treated (n=5) and non treated (n=5). Segments of the jejunum were used as controls (C), or as venous ischemia (VIsc), which were subjected to 2h of ischemia followed by 2 or 12h of reperfusion. C samples were collected at time zero (prior to ischemia) and VIsc samples were collected at 2h of ischemia and at 2 and 12h of reperfusion. TUNEL positive apoptotic cells were counted in 10 microscopical fields in deep mucosa from each horse throughout the time course. After 12h of reperfusion, the number of apoptotic cells in treated group were significantly lower than in untreated animals, indicating that hydrocortisone inhibits apoptosis. These results indicate that hydrocortisone has a beneficial effects favoring the maintenance of jejunal integrity in horses with ischemia and reperfusion injuries by preventing apoptotic cell death.
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