A framework for knowledge-based temporal abstraction
Artificial Intelligence
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Regression Modeling Strategies
Regression Modeling Strategies
Discovery and inclusion of SOFA score episodes in mortality prediction
Journal of Biomedical Informatics
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
Severity Evaluation Support for Burns Unit Patients Based on Temporal Episodic Knowledge Retrieval
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
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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Objectives: The current established mortality predictive models in the intensive care rely only on patient information gathered within the first 24hours of admission. Recent research demonstrated the added prognostic value residing in the sequential organ-failure assessment (SOFA) score which quantifies on each day the cumulative patient organ derangement. The objective of this paper is to develop and study predictive models that also incorporate univariate patterns of the six individual organ systems underlining the SOFA score. A model for a given day d predicts the probability of in-hospital mortality. Materials and methods: We use the logistic framework to combine a summary statistic of the historic SOFA information for a patient together with selected dummy variables indicating the occurrence of univariate frequent temporal patterns of individual organ system functioning. We demonstrate the application of our method to a large real-life data set from an intensive care unit (ICU) in a teaching hospital. Model performance is tested in terms of the AUC and the Brier score. Results: An algorithm for categorization, discovery, and selection of univariate patterns of individual organ scores and the induction of predictive models. The case-study resulted in six daily models corresponding to days 2-7. Their AUC ranged between 0.715 and 0.794 and the Brier scores between 0.161 and 0.216. Models using only admission data but recalibrated for days 2-7 generated AUC ranging between 0.643 and 0.761 and Brier scores ranged between 0.175 and 0.230. Conclusions: The results show that temporal organ-failure episodes improve predictions' quality in terms of both discrimination and calibration. In addition, they enhance the interpretability of models. Our approach should be applicable to many other medical domains where severity scores and sub-scores are collected.