Authors:
Garazi Artola
1
;
Nekane Larburu
2
;
Roberto Álvarez
2
;
Vanessa Escolar
3
;
Ainara Lozano
3
;
Benjamin Juez
3
and
Jon Kerexeta
1
Affiliations:
1
Vicomtech Research Centre, Mikeletegi Pasalekua 57, 20009, San Sebastian and Spain
;
2
Vicomtech Research Centre, Mikeletegi Pasalekua 57, 20009, San Sebastian, Spain, Biodonostia Health Research Institute, P. Doctor Begiristain s/n, 20014 San Sebastian and Spain
;
3
Hospital Universitario de Basurto (Osakidetza Health Care System), Avda Montevideo 18, 48013, Bilbao and Spain
Keyword(s):
Heart Failure, Hospital Admission, Open Data, Environmental Factors.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Healthcare Management Systems
;
ICT, Ageing and Disability
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Heart failure (HF) is defined as the incapacity of the heart to pump sufficiently to maintain blood flow to meet the body's needs. Often, this causes sudden worsening of the signs and symptoms of heart failure (decompensations), which may lead on hospital admissions, deteriorating patients’ quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF. In this paper, we describe the development and results of a methodology to determine the effect of environmental factors on HF decompensations by means of hospital admissions. For that, a total number of 8338 hospitalizations of 5343 different patients, and weather and air quality information from open databases have been considered. The results demonstrate that several environmental factors, such as weather temperature, have an impact on the HF related hospital admissions rate,
and hence, on HF decompensations and patientś quality of life. The next steps are first to predict the number of hospital admissions based on the presented study, and second, the inclusion of these environmental factors on predictive models to assess the risk of decompensation of an ambulatory patient in real time.
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