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.

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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

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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.

Ann. Intern. Med. 153(9), 600–606 (2010) 2. : Electronic medical records for genetic research: results of the eMERGE consortium. Science Translational Medicine 3(79), 79re1 (2011) 3. : PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 26(9), 1205–1210 (2010) 4. : Defining a comprehensive verotype using electronic health records for personalized medicine. J. Am. Med. Inform. Assoc. 20(e2), e232–e238 (2013) 5. : Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

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