@inproceedings{969a79b1383442c3bae0c7b70122e1fe,
title = "Considering temporal preferences and probabilities in guideline interaction analysis",
abstract = "The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. Practically speaking, such interactions occur in time. The GLARE project explicitly provides temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, (i) the actions in the guidelines can be executed by physicians at different times with different preferences, and that (ii) the effects of such actions have a probabilistic distribution in time. In our approach, physicians may investigate what are the preferences of their choices on the execution-time of guideline actions, and the probabilities that their effects temporally intersect (interactions may occur only in case effects intersect in time).",
keywords = "CIG interactions, Comorbidities, Preferences, Probabilities, Temporal reasoning",
author = "Paolo Terenziani and Antonella Andolina",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 17th Conference on Artificial Intelligence in Medicine, AIME 2019 ; Conference date: 26-06-2019 Through 29-06-2019",
year = "2019",
doi = "10.1007/978-3-030-21642-9\_16",
language = "English",
isbn = "9783030216412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "120--124",
editor = "David Ria{\~n}o and Szymon Wilk and \{ten Teije\}, Annette",
booktitle = "Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings",
address = "Germany",
}