Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients.

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

Risk Estimation for ICU Patients with Personalized Anomaly-Encoded Bedside Patient Data.

We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.