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Application of Sequence Data Mining for Adverse Event Prediction and Action Recommendation

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Potpourri

Tagged Division

Computing & Information Technology

Page Count

9

Page Numbers

26.224.1 - 26.224.9

DOI

10.18260/p.23563

Permanent URL

https://peer.asee.org/23563

Download Count

41

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Paper Authors

biography

Reza Sanati-Mehrizy Utah Valley University

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Reza Sanati-Mehrizy is a professor of Computer Science Department at Utah Valley University, Orem, Utah. He received his M.S. and Ph.D. in Computer Science from the University of Oklahoma, Norman, Oklahoma. His research focuses on diverse areas such as: Database Design, Data Structures, Artificial Intelligence, Robotics, Computer Aided Manufacturing, Data Mining, Data Warehousing, and Machine Learning.

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Ali Sanati-Mehrizy

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Dr. Ali Sanati-Mehrizy is a Pediatric resident physician at Rutgers University - New Jersey Medical School in Newark, NJ. He is a graduate of the Milton S. Hershey Pennsylvania State University College of Medicine. He completed his undergraduate studies in Biology from the University of Utah. His research interests are varied and involve pediatric hematology and oncology as well as higher education curricula, both with universities and medical schools.

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Paymon Sanati-Mehrizy Icahn School of Medicine at Mount Sinai

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Paymon Sanati-Mehrizy is currently a medical student at the Icahn School of Medicine at Mount Sinai. He completed his undergraduate studies in Biology from the University of Pennsylvania in May 2012. Currently, his research interests consist of higher education curricula, both with universities and medical schools.

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Afsaneh Minaie Utah Valley University

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Afsaneh Minaie is a professor of Computer Engineering at Utah Valley University. She received her B.S., M.S., and Ph.D. all in Electrical Engineering from University of Oklahoma. Her research interests include gender issues in the academic sciences and engineering fields, Embedded Systems Design, Mobile Computing, Wireless Sensor Networks, Nanotechnology, Data Mining and Databases.

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Abstract

Application of Sequence Data Mining for Adverse Event Prediction and Action Recommendation in Healthcare SystemMany real-life data mining applications use sequence data modeling in which data isrepresented as a sequence. A sequence is an ordered list of events (t1,e1), (t2,e2), …,(tn,en)where ti represents time and ei represents the event taking place at time ti. ei takes placebefore ei+1 for 1≤ i ≤ n-1. This model can be used in data mining, called sequence datamining, to predict certain event that may take place at a specific time.Sequence data mining has a wide range of applications in the data mining field. This datamining technique can be used for prediction of adverse events and recommend properactions to be taken as needed. For the aviation safety, the future of a flight can bepredicted as a sequence and proper action can be recommended to avoid dangeroussituations that a flight may get into otherwise. In the health care system, the future of abacterial infection can be predicted and proper medicine can be prescribed for differentsituations to bring the patient’s illness to an end. In the marketing, customer shopping canbe monitored and certain action can be taken, such as mailing coupons, to encourage thecustomer for further shopping of relevant products. In the real-life situations such asmanufacturing plants, sensors’ data can be analyzed to control operations and predictdangerous situations and recommend proper actions. This paper discusses a new technique for implementation of sequence data mining andits applications for a number of different cases in healthcare industry.

Sanati-Mehrizy, R., & Sanati-Mehrizy, A., & Sanati-Mehrizy, P., & Minaie, A. (2015, June), Application of Sequence Data Mining for Adverse Event Prediction and Action Recommendation Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23563

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