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Comorbidity constructs for patients with congestive heart failure and their effect on hospital outcomes of care

Published: 05 June 2019 Publication History

Abstract

We present a study of the cumulative effect of comorbidities on hospital length of stay (LOS), and hospital mortality, for patients with Congestive Heart Failure (CHF). This condition can be life-threatening, while a burdened disease profile significantly increases the risk for negative outcomes. Our hypothesis is that coexisting conditions often co-interact, in various ways, and these interactions can have a variable effect on outcomes; clinical decision makers should, therefore, be able to recognize these joint effects. In order to study the CHF comorbidities, we used medical claims data from CMS. Firstly, we conducted cluster analysis to find the common hospital comorbidities for CHF admissions. We then extracted the most frequent cluster: {metabolism disorders, anemia, hypertension with complications, coronary atherosclerosis, chronic kidney disease} and calculated conditional probabilities in a modular manner for all combinations within this cluster. We furthermore estimated the cumulative effect of these comorbidity combinations on the two outcomes under study in a step by step, modular manner. Results were visualized with directed acyclic graphs.

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  • (2022)Diagnosing Multiple Chronic Diseases based on Machine Learning Techniques: Review, Challenges and Futuristic Approach2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IOT53935.2022.9767930(1-6)Online publication date: 10-Mar-2022

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cover image ACM Other conferences
PETRA '19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments
June 2019
655 pages
ISBN:9781450362320
DOI:10.1145/3316782
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 June 2019

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  1. clustering
  2. comorbidities
  3. congestive heart failure
  4. health informatics

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  • (2022)Diagnosing Multiple Chronic Diseases based on Machine Learning Techniques: Review, Challenges and Futuristic Approach2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IOT53935.2022.9767930(1-6)Online publication date: 10-Mar-2022

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