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We compare the dynamic Bayesian network and k-nearest neighbor-based predictors for the occurrence of acute hypotensive episodes (AHE) with respect to various data conditions (size, class balance ratio) and problem definition settings (lag, lead time). From our dataset extracted from the large ICU physiological waveform repository of MIMIC2 database, we find that both models are effective for predicting...
In the setting of the PhysioNet/CinC Challenge 2012 Event 1, a new method to predict in hospital mortality in the Intensive Care Units (ICU) is proposed. The predictor, retrieved by Simple Correspondence Analysis (SCA), is based on a combination of clinical and laboratory data with more traditional score systems such as APACHE-II and SAPS-II. Information from records out of 12000 ICU patients was...
In this paper, we explore the application of motif discovery (i.e., the discovery of short characteristic patterns in a time series) to the clinical challenge of predicting intensive care unit (ICU) mortality. As part of the Physionet/CinC 2012 challenge, we present an approach that identifies and integrates information in motifs that are statistically over-or under-represented in ICU time series...
In order to better guide training according to the training test data, this paper uses the method of the time series data analysis, to obtain the knowledge rules from the training data of rowing through analysis. Namely the basis of different training content and the test project, we establish the different threshold value, thus obtain the athlete training effect of different time: The training excessively...
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