Detecting possibly incorrect medication using an unlikely co-occurrences measure

Ionut Emil Iacob; Hameed Jimoh; Abdullah Al Mamun

doi:10.1109/ECAI.2017.8166420

Detecting possibly incorrect medication using an unlikely co-occurrences measure

Ionut Emil Iacob
, Hameed Jimoh
, Abdullah Al Mamun

Mathematical Sciences

Georgia Southern University

Research output: Contribution to book or proceeding › Conference article › peer-review

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.

Original language	English
Title of host publication	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
Number of pages	6
ISBN (Electronic)	9781509064571
DOIs	https://doi.org/10.1109/ECAI.2017.8166420
State	Published - Dec 4 2017
Event	9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017 - Targoviste, Romania Duration: Jun 29 2017 → Jul 1 2017

Publication series

Name	Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017
Volume	2017-January

Conference

Conference	9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017
Country/Territory	Romania
City	Targoviste
Period	06/29/17 → 07/1/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering
Control and Optimization
Instrumentation

Keywords

Data analysis
Similarity measure
Unlikely co-occurrences

Access to Document

10.1109/ECAI.2017.8166420

Cite this

Iacob, I. E., Jimoh, H., & Al Mamun, A. (2017). Detecting possibly incorrect medication using an unlikely co-occurrences measure. In Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017 (pp. 1-6). (Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECAI.2017.8166420

Iacob, Ionut Emil ; Jimoh, Hameed ; Al Mamun, Abdullah. / Detecting possibly incorrect medication using an unlikely co-occurrences measure. Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017).

@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",

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language = "English",

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}

Iacob, IE, Jimoh, H & Al Mamun, A 2017, Detecting possibly incorrect medication using an unlikely co-occurrences measure. in Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017, Targoviste, Romania, 06/29/17. https://doi.org/10.1109/ECAI.2017.8166420

Detecting possibly incorrect medication using an unlikely co-occurrences measure. / Iacob, Ionut Emil; Jimoh, Hameed; Al Mamun, Abdullah.
Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017; Vol. 2017-January).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Detecting possibly incorrect medication using an unlikely co-occurrences measure

AU - Iacob, Ionut Emil

AU - Jimoh, Hameed

AU - Al Mamun, Abdullah

PY - 2017/12/4

Y1 - 2017/12/4

N2 - 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.

AB - 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.

KW - Data analysis

KW - Similarity measure

KW - Unlikely co-occurrences

UR - https://www.scopus.com/pages/publications/85043339856

U2 - 10.1109/ECAI.2017.8166420

DO - 10.1109/ECAI.2017.8166420

M3 - Conference article

AN - SCOPUS:85043339856

T3 - Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017

SP - 1

EP - 6

BT - Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017

Y2 - 29 June 2017 through 1 July 2017

ER -

Iacob IE, Jimoh H, Al Mamun A. Detecting possibly incorrect medication using an unlikely co-occurrences measure. In Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017). doi: 10.1109/ECAI.2017.8166420

Detecting possibly incorrect medication using an unlikely co-occurrences measure

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

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