By John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek
This e-book constitutes the refereed complaints of the fifteenth convention on synthetic 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: approach mining and phenotyping; facts mining and computer studying; temporal facts mining; uncertainty and Bayesian networks; textual content mining; prediction in medical 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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Additional resources for Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
In such cases, local anomalies happen inevitably. Unlike global anomalous patient traces of CTPs, which are largely deviated from normal ones, local anomalies are unusual treatment events existing in some subsegments in patient traces of CTPs . 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.
The generated probabilistic topic model provides an accurate description of CTPs by combining diﬀerent classes of distributions. In particular, it recognizes patient traces as a probabilistic combination of underlying treatment patterns, and describes treatment patterns as a probabilistic combination of various clinical events . Based on the derived topic model, a predictive monitoring service of local anomalies in CTPs is provided on ongoing patient traces, which looks insight into CTPs to map the prediction task to a classiﬁcation task such that unusual variant execution of clinical activities can be timely predicted in an ongoing patient trace.
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.