Patterns of heart attacks

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.

Bibliographic Details
Main Author: Shenk, Kimberly N
Other Authors: Natasha Markuzon and Dimitris J. Bertsimas.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1721.1/61198
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author Shenk, Kimberly N
author2 Natasha Markuzon and Dimitris J. Bertsimas.
author_facet Natasha Markuzon and Dimitris J. Bertsimas.
Shenk, Kimberly N
author_sort Shenk, Kimberly N
collection MIT
description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.
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spelling mit-1721.1/611982020-11-24T01:56:13Z Patterns of heart attacks Shenk, Kimberly N Natasha Markuzon and Dimitris J. Bertsimas. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 66-68). Myocardial infarction is a derivative of heart disease that is a growing concern in the United States today. With heart disease becoming increasingly predominant, it is important to not only take steps toward preventing myocardial infarction, but also towards predicting future myocardial infarctions. If we can predict that the dynamic pattern of an individual's diagnostic history matches a pattern already identified as high-risk for myocardial infarction, more rigorous preventative measures can be taken to alter that individual's trajectory of health so that it leads to a better outcome. In this paper we utilize classification and clustering data mining methods concurrently to determine whether a patient is at risk for a future myocardial infarction. Specifically, we apply the algorithms to medical claims data from more than 47,000 members over five years to: 1) find different groups of members that have interesting temporal diagnostic patterns leading to myocardial infarction and 2) provide out-of-sample predictions of myocardial infarction for these groups. Using clustering methods in conjunction with classification algorithms yields improved predictions of myocardial infarction over using classification alone. In addition to improved prediction accuracy, we found that the clustering methods also effectively split the members into groups with different and meaningful temporal diagnostic patterns leading up to myocardial infarction. The patterns found can be a useful profile reference for identifying patients at high-risk for myocardial infarction in the future. by Kimberly N. Shenk. S.M. 2011-02-23T14:28:14Z 2011-02-23T14:28:14Z 2010 2010 Thesis http://hdl.handle.net/1721.1/61198 701084269 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 76 p. application/pdf Massachusetts Institute of Technology
spellingShingle Operations Research Center.
Shenk, Kimberly N
Patterns of heart attacks
title Patterns of heart attacks
title_full Patterns of heart attacks
title_fullStr Patterns of heart attacks
title_full_unstemmed Patterns of heart attacks
title_short Patterns of heart attacks
title_sort patterns of heart attacks
topic Operations Research Center.
url http://hdl.handle.net/1721.1/61198
work_keys_str_mv AT shenkkimberlyn patternsofheartattacks