Reduction of Readmissions to Hospitals Based on Actionable Knowledge Discovery and Personalization

Mamoun Almardini , Ayman Hajja , Zbigniew W. Raś , Lina Clover , David Olaleye , Youngjin Park , Jay Paulson , Yang Xiao

Abstract

In this work, we define procedure paths as the sequence of procedures that a given patient undertakes to reach a desired treatment. In addition to its value as a mean to inform the patient of his or her course of treatment, being able to identify and anticipate procedure paths for new patients is an essential task for examining and evaluating the entire course of treatments in advance, and ultimately rectifying undesired procedure paths accordingly. In this paper, we first introduce two approaches for anticipating the state of the patient that he or she will end up in after performing some procedure p; the state of the patient will consequently indicate the following procedure that the patient is most likely to undergo. By clustering patients into subgroups that exhibit similar properties, we improve the predictability of their procedure paths, which we evaluate by calculating the entropy to measure the level of predictability of following procedure. The clustering approach used is essentially a way of personalizing patients according to their properties. The approach used in this work is entirely novel and was designed specifically to address the twofold problem of first being able to predict following procedures for new patients with high accuracy, and secondly being able to construct such groupings in a way that allows us to identify exactly what it means to transition from one cluster to another. Then, we further devise a metric system that will evaluate the level of desirability for procedures along procedure paths, which we would subsequently map to a metric system for the extracted clusters. This will allow us to find desired transitions between patients in clusters, which would result in reducing the number of anticipated readmissions for new patients.
Author Mamoun Almardini - [University of North Carolina at Charlotte (UNC Charlotte)]
Mamoun Almardini,,
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, Ayman Hajja - [The University of North Carolina at Charlotte]
Ayman Hajja,,
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, Zbigniew W. Raś (FEIT / IN)
Zbigniew W. Raś,,
- The Institute of Computer Science
, Lina Clover - [SAS Institute, Inc.]
Lina Clover,,
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, David Olaleye - [SAS Institute, Inc.]
David Olaleye,,
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, Youngjin Park - [SAS Institute, Inc.]
Youngjin Park,,
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, Jay Paulson - [SAS Institute, Inc.]
Jay Paulson,,
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, Yang Xiao - [SAS Institute, Inc.]
Yang Xiao,,
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Pages39-55
Publication size in sheets0.8
Book Kozielski Stanisław, Mrozek Dariusz, Kasprowski Paweł, Małysiak-Mrozek Bożena, Kostrzewa Daniel (eds.): Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery, Communications in Computer and Information Science, vol. 613, 2016, Springer International Publishing, ISBN 978-3-319-34098-2, 738 p., DOI:10.1007/978-3-319-34099-9
Keywords in EnglishPersonalization – Side-effects – Clustering
ASJC Classification2600 General Mathematics; 1700 General Computer Science
DOIDOI:10.1007/978-3-319-34099-9_3
URL http://link.springer.com/chapter/10.1007%2F978-3-319-34099-9_3
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 29-01-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 29-01-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 5; WoS Citations = 3; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.371
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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