Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Constructing (Almost) phylogenetic trees from developmental sequences data
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Regression Modeling Strategies
Regression Modeling Strategies
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Mortality assessment in intensive care units via adverse events using artificial neural networks
Artificial Intelligence in Medicine
Modern Applied Statistics with S
Modern Applied Statistics with S
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
Guest Editorial: Intelligent data analysis in biomedicine
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Rating organ failure via adverse events using data mining in the intensive care unit
Artificial Intelligence in Medicine
Discovery and Integration of Organ-Failure Episodes in Mortality Prediction
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Repeated prognosis in the intensive care: how well do physicians and temporal models perform?
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Assessing and combining repeated prognosis of physicians and temporal models in the intensive care
Artificial Intelligence in Medicine
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Predicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. We propose a novel method for mortality prediction that, in addition to admission-related data, takes advantage of daily data as well. The method is characterized by the data-driven discovery of temporal patterns, called episodes, of the organ failure scores and by embedding them in the familiar logistic regression framework for prediction. Our method results in a set of D logistic regression models, one for each of the first D days of Intensive Care Unit stay. A model for day d=