By John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek
This e-book constitutes the refereed court cases 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 offered have been rigorously reviewed and chosen from ninety nine submissions. The papers are geared up within the following topical sections: technique mining and phenotyping; info mining and computing device studying; temporal info mining; uncertainty and Bayesian networks; textual content mining; prediction in scientific perform; and data illustration and guidelines.
Read Online or Download Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings PDF
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Additional info for Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
As shown in Figure 2(B), the curves of unexpected and delay events increase linearly, the curve of early events increases at ﬁrst with the increase of νlow , and then remain stable with the further increase of νlow . 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.
T > t. , patient id: 476104-3) out of the selected patient traces. This observation, in turn, provides a starting point for the analysis of the respective CTP. The reasons for delay occurred execution of treatment activity A21 have Predictive Monitoring of Local Anomalies in Clinical Treatment Processes 29 to be determined as root causes for non-compliant treatment behavior in its host patient trace. Based on the deﬁnitions above, we can detect local anomalies taking place in complete patient traces, and then use the detected anomalies to build a classiﬁcation model such that ongoing patient traces can be checked if local anomalies could occur and in which type.
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