research-article

Using electronic health records to predict severity of condition for congestive heart failure patients

Authors:

Costas Sideris,

Behnam Shahbazi,

Mohammad Pourhomayoun,

Nabil Alshurafa,

Majid SarrafzadehAuthors Info & Claims

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

Pages 1187 - 1192

https://doi.org/10.1145/2638728.2638815

Published: 13 September 2014 Publication History

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Abstract

We propose a novel way to design an analytics engine based exclusively on electronic health records (EHR). We focus our efforts on Congestive Heart Failure (CHF) patients, although our approach could be extended to other chronic conditions. Our goal is to construct statistical models that predict a CHF patient's length of stay and by extension the severity of his/her condition. We show that it is possible to predict length of hospital stay based on physiological data collected from the first day of hospitalization. Using 10-fold cross validation we achieve accurate predictions with a root mean square error of 3.3 days for hospital stays that are less than 15 days in duration. We also propose a clustering of patients that organizes them to risk groups according to their estimated severity of condition.

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Alshurafa NStump TRomano CF. Pfammatter ALin AHester JHedeker DForman ESpring B(2023)Rationale and design of the SenseWhy project: A passive sensing and ecological momentary assessment study on characteristics of overeating episodesDIGITAL HEALTH10.1177/205520762311583149Online publication date: 27-Apr-2023
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Saleemi MAnjum MRehman M(2020)Ubiquitous healthcare: a systematic mapping studyJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02513-x14:5(5021-5046)Online publication date: 26-Sep-2020
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Index Terms

Using electronic health records to predict severity of condition for congestive heart failure patients
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

September 2014

1409 pages

ISBN:9781450330473

DOI:10.1145/2638728

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2014

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UbiComp '14

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Alshurafa NStump TRomano CF. Pfammatter ALin AHester JHedeker DForman ESpring B(2023)Rationale and design of the SenseWhy project: A passive sensing and ecological momentary assessment study on characteristics of overeating episodesDIGITAL HEALTH10.1177/205520762311583149Online publication date: 27-Apr-2023
https://doi.org/10.1177/20552076231158314
Popa IBurlacu AGavrilescu ODranga MPrelipcean CMihai C(2021)A new approach to predict ulcerative colitis activity through standard clinical–biological parameters using a robust neural network modelNeural Computing and Applications10.1007/s00521-021-06055-x33:21(14133-14146)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s00521-021-06055-x
Saleemi MAnjum MRehman M(2020)Ubiquitous healthcare: a systematic mapping studyJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02513-x14:5(5021-5046)Online publication date: 26-Sep-2020
https://doi.org/10.1007/s12652-020-02513-x
Siegers KVan Mechelen MVanattenhoven JOliver NCzerwinski M(2017)Mapping the health technology needs of congestive heart failure patientsProceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare10.1145/3154862.3154916(266-271)Online publication date: 23-May-2017
https://dl.acm.org/doi/10.1145/3154862.3154916
CHENG YLin YChiang KTseng V(2017)Mining Sequential Risk Patterns from Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary DiseaseIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2017.2657802(1-1)Online publication date: 2017
https://doi.org/10.1109/JBHI.2017.2657802
Osop HSahama T(2016)Electronic health records: Improvement to healthcare decision-making2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2016.7749474(1-6)Online publication date: Sep-2016
https://doi.org/10.1109/HealthCom.2016.7749474
Osop HSahama T(2016)Quality evidence, quality decisions: Ways to improve security and privacy of EHR systems2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2016.7749424(1-6)Online publication date: Sep-2016
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Sideris CPourhomayoun MKalantarian HSarrafzadeh M(2016)A flexible data-driven comorbidity feature extraction frameworkComputers in Biology and Medicine10.1016/j.compbiomed.2016.04.01473:C(165-172)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2016.04.014

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