By John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek
This ebook constitutes the refereed court cases of the fifteenth convention on synthetic Intelligence in medication, AIME 2015, held in Pavia, Italy, in June 2015. the nineteen revised complete and 24 brief papers provided have been conscientiously reviewed and chosen from ninety nine submissions. The papers are geared up within the following topical sections: approach mining and phenotyping; info mining and desktop 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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Extra resources for Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
A⇒B means that each occurrence of items A in a sequence is followed by an occurrence of items B in the same sequence. Temporal order is not always sufficient for the extraction of interesting rules. Sometimes, one needs to use additional background knowledge to constraint the mining of patterns or rules with characteristics that are assumed to define or enhance their usefulness and significance. : the customer socio-professional profile). Likewise, in , the proposition is to mine sequential patterns based on the existence of a relationship between events reported in the sequences.
Active learning for clinical text classification: is it better than random sampling? Journal of the American Medical Informatics Association (2011), 2012:amiajnl-2011-000648 33. : Supervised machine learning and active learning in classification of radiology reports. Journal of the American Medical Informatics Association (2014) 34. : LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011) 35. : Support vector machine active learning with applications to text classification.
In particular, we adopt SVM to build classiﬁers for the variation prediction. As a result, 25 unexpected events, 3 early events, 326 delay events, and 19 absent events for the target clinical activities are predicted by the proposed method. Then, we ask three clinicians to validate the detected variations using a major voting strategy. As shown in Figure 4, almost all classiﬁers have achieved a precision that is greater than 70%. , even have achieved 100% precisions. It indicates that the predicated variations of the proposed approach are well recognized by clinicians.
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