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

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

ISBN-10: 3319195506

ISBN-13: 9783319195506

ISBN-10: 3319195514

ISBN-13: 9783319195513

This booklet constitutes the refereed court cases of the fifteenth convention on man made Intelligence in drugs, AIME 2015, held in Pavia, Italy, in June 2015. the nineteen revised complete and 24 brief papers awarded have been conscientiously reviewed and chosen from ninety nine submissions. The papers are geared up within the following topical sections: strategy mining and phenotyping; information mining and computing device studying; temporal info mining; uncertainty and Bayesian networks; textual content mining; prediction in scientific perform; and data illustration and guidelines.

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

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It indicates that the predicated variations of the proposed approach are well recognized by clinicians. Table 1 depicts the overall precisions. 4%, which indicates the feasibility of the proposed approach for local anomaly prediction in CTPs. Predictive Monitoring of Local Anomalies in Clinical Treatment Processes 33 Fig. 3. Prediction accuracy of classifiers on the experimental log Fig. 4. Precisions achieved by the trained classifiers on the selected 100 patient traces with the efforts of human evaluation Table 1.

The difference is that their training samples are balanced between unusual events and normal cases, which means that the execution states of the well predicted activities exist for the balanced training samples, which is obvious for the learning algorithm to derive. e. 38%, respectively. According to the results, all three machine learning algorithms have achieved an overall prediction accuracy of well over 80% on the experimental log. Among three algorithms, SVM generally performs the best although it is marginally.

If we only take into account the perplexity, we probably select the maximum K of a given range, which may make the learned model over-fitting. Thus, we balanced the above-mentioned approach by a simple way; that is, if the reducing ratio of perplexity is less than τ , we do not select a larger K. In practice, we set τ to be 10% according to experiment analysis. In this study, we empirically choose the number of patterns K = 3 for the experimental log, where the perplexity seems to decrease rapidly and appear to settle down.

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Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings by John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek


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