By John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek
This publication 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 provided have been rigorously reviewed and chosen from ninety nine submissions. The papers are prepared within the following topical sections: technique mining and phenotyping; information mining and laptop studying; temporal information mining; uncertainty and Bayesian networks; textual content mining; prediction in scientific perform; and information illustration and guidelines.
Read 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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Extra info for Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
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.
H. Holmes et al. ): AIME 2015, LNAI 9105, pp. 25–34, 2015. 1007/978-3-319-19551-3_4 26 Z. Huang et al. and consistently present. For CTP analysis, it is imperative to predict potential local anomalies from observations in a maximally-informative manner. Once these unusual events are timely predicted, a health-care organization can either update its CTP speciﬁcations to cover the respective case, or it can adjust an ongoing patient trace to enforce best clinical practice execution. In this way, local anomaly prediction is a central piece in the puzzle of advancing health-care organizations towards eﬀective and eﬃcient CP management and health service delivery.
In: Proceedings of the AMIA Symposium 2001, p. 662. American Medical Informatics Association (2001) 16. : A survey of SNOMED CT direct users, 2010: impressions and preferences regarding content and quality. Journal of the American Medical Informatics Association 18(suppl. 1), i36–i44 (2011) 17. : Vaidurya–a concept-based, context-sensitive search engine for clinical guidelines. American Medical Informatics Association (2004) An Active Learning Framework for Efficient Condition Severity Classification 23 18.