@inproceedings{c9c2e8c0325642dea3bdef1338796e32,
title = "Detecting possibly incorrect medication using an unlikely co-occurrences measure",
abstract = "The rapid development of statistical learning methods and an increase in computational power and medical data repositories have significantly contributed to a plethora of applications for medical diagnostics prediction. In this paper we present a solution for an interesting problem which, surprisingly enough given its importance, has not gained much attention: detecting unexpected co-occurrences of data features. As standard data analysis methods do not directly produce a solution for the problem, we propose a measure for direct discovery of unlikely co-occurrences of data instances. We show how our method can be directly applied to detect possible incorrect medication for diabetes patients.",
keywords = "Data analysis, Similarity measure, Unlikely co-occurrences",
author = "Iacob, {Ionut Emil} and Hameed Jimoh and {Al Mamun}, Abdullah",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017 ; Conference date: 29-06-2017 Through 01-07-2017",
year = "2017",
month = dec,
day = "4",
doi = "10.1109/ECAI.2017.8166420",
language = "English",
series = "Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017",
address = "United States",
}
