Download Artificial Intelligence in Medicine: 15th Conference on by John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek PDF

By John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek

This e-book constitutes the refereed lawsuits of the fifteenth convention on man made Intelligence in medication, AIME 2015, held in Pavia, Italy, in June 2015. the nineteen revised complete and 24 brief papers awarded have been rigorously reviewed and chosen from ninety nine submissions. The papers are prepared within the following topical sections: strategy mining and phenotyping; facts mining and computer studying; temporal facts mining; uncertainty and Bayesian networks; textual content mining; prediction in scientific perform; and information illustration and guidelines.

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Extra info for Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings

Example text

Furthermore, its FPR levels were mostly unstable until the end of the trials. 3%. Considering the TPR and FPR measures together, we can see how efficient the AL methods were, in spite of the unbalanced mix of condition severities in our data set. The cumulative number of severe conditions acquired for each trial is shown in Figure 5. By the fifth trial, Exploitation and Combination_XA outperformed the other selection methods (both lines overlap in Figure 5). We observed that after 23 trials (115 conditions) both of CAESAR-ALE’s methods acquired 73 out of 82 severe conditions in the pool, as compared to 42 trials (210 conditions) for SVM-Margin and 60 trials (300 conditions) for random.

The experiment’s steps (below) are repeated until the entire pool is acquired: (1) Induce the initial classification model from the initial training set. (2) Evaluate the classification model's initial performance using the test set. (3) Introduce unlabeled conditions to the pool for the selective sampling method. The five most informative conditions are selected according to each method’s criteria and then sent to the medical expert for labeling. (4) Add acquired conditions to the training set (removing them from the pool).

05, there are 3776 unexpected events, 986 early events, and 38825 delay events detected form the experimental log. Based on the identified local anomalies, classifiers for predic- Fig. 2. Variation detection using the unstable angina data-set tive monitoring of typical clinical activities can be generated. To this end, we selectd several significant patient features to generate samples for classification. As mentioned above, there are 144 patient features in the collected event log. But not all features are closely related with the treatment process of unstable angina, and it should select significant features for the unstable angina patients to improve the accuracy of predictive monitoring.

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