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Mining disproportional itemsets for characterizing groups of heart failure patients from administrative health records
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- Karlsson, Isak (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Stockholms universitet, Institutionen för data- och systemvetenskap
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- Papapetrou, Panagiotis (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Stockholms universitet, Institutionen för data- och systemvetenskap
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- Asker, Lars (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Stockholms universitet, Institutionen för data- och systemvetenskap
- show more...
- Persson, Hans E. (author)
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- (creator_code:org_t)
- 2017-06-21
- 2017
- English.
- In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450352277 ; , s. 394-398
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- Heart failure is a serious medical conditions involving decreased quality of life and an increased risk of premature death. A recent evaluation by the Swedish National Board of Health and Welfare shows that Swedish heart failure patients are often undertreated and do not receive basic medication as recommended by the national guidelines for treatment of heart failure. The objective of this paper is to use registry data to characterize groups of heart failure patients, with an emphasis on basic treatment. Towards this end, we explore the applicability of frequent itemset mining and disproportionality analysis for finding interesting and distinctive characterizations of a target group of patients, e.g., those who have received basic treatment, against a control group, e.g., those who have not received basic treatment. Our empirical evaluation is performed on data extracted from administrative health records from the Stockholm County covering the years 2010--2016. Our findings suggest that frequency is not always the most appropriate measure of importance for frequent itemsets, while itemset disproportionality against a control group provides alternative rankings of the extracted itemsets leading to some medically intuitive characterizations of the target groups.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- frequent itemsets
- disproportionality analysis
- heart failure
- Computer and Systems Sciences
- data- och systemvetenskap
Publication and Content Type
- ref (subject category)
- kon (subject category)
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- Asker, Lars
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- NATURAL SCIENCES
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- Royal Institute of Technology
- Stockholm University
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