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Content Provider | IEEE Xplore Digital Library |
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Author | Johnson, A.E.W. Dunkley, N. Mayaud, L. Tsanas, A. Kramer, A.A. Clifford, G.D. |
Copyright Year | 2012 |
Description | Author affiliation: University of Oxford, Oxford, United Kingdom (Johnson, A.E.W.; Dunkley, N.; Mayaud, L.; Tsanas, A.; Clifford, G.D.) || Cerner Corporation, Vienna, VA, United States (Kramer, A.A.) |
Abstract | An intensive care unit mortality prediction model for the PhysioNet/Computing in Cardiology Challenge 2012 using a novel Bayesian ensemble learning algorithm is described. Methods: Data pre-processing was automatically performed based upon domain knowledge to remove artefacts and erroneous recordings, e.g. physiologically invalid entries and unit conversion errors. A range of diverse features was extracted from the original time series signals including standard statistical descriptors such as the minimum, maximum, median, first, last, and the number of values. A new Bayesian ensemble scheme comprising 500 weak learners was then developed to classify the data samples. Each weak learner was a decision tree of depth two, which randomly assigned an intercept and gradient to a randomly selected single feature. The parameters of the ensemble learner were determined using a custom Markov chain Monte Carlo sampler. Results: The model was trained using 4000 observations from the training set, and was evaluated by the organisers of the competition on two new datasets with 4000 observations each (set b and set c). The outcomes of the datasets were unavailable to the competitors. The competition was judged on two events by two scores. Score 1 was the minimum of the positive predictive value and sensitivity for binary model predictions, and the model achieved 0.5310 and 0.5353 on the unseen datasets. Score 2, a range-normalized Hosmer-Lemeshow C statistic, evaluated to 26.44 and 29.86. The model was re-developed using the updated data sets from phase 2 after the competition, and achieved a score 1 of 0.5374 and a score 2 of 18.20 on set c. Conclusion: The proposed prediction model performs favourably on both the provided and hidden data sets (set A and set B), and has the potential to be used effectively for patient-specific predictions. |
Starting Page | 249 |
Ending Page | 252 |
File Size | 217957 |
Page Count | 4 |
File Format | |
ISBN | 9781467320764 |
ISSN | 2325887X |
e-ISBN | 9781467320771 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-09-09 |
Publisher Place | Poland |
Access Restriction | Subscribed |
Rights Holder | Creative Commons Attribution License 2.5 (CCAL) |
Subject Keyword | Predictive models Data models Vegetation Training Biomedical monitoring Feature extraction |
Content Type | Text |
Resource Type | Article |
Subject | Computer Science Cardiology and Cardiovascular Medicine |
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