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Revista Colombiana de Cardiología
Print version ISSN 0120-5633
Abstract
ESCOLAR, Vanessa et al. Prediction of heart failure decompensations using artificial intelligence and machine learning techniques. Rev. Colomb. Cardiol. [online]. 2022, vol.29, n.4, pp.431-440. Epub Nov 08, 2022. ISSN 0120-5633. https://doi.org/10.24875/rccar.21000013.
Introduction:
Heart failure (HF) is a major concern in public health. We have used artificial intelligence to analyze information and improve patient outcomes.
Method:
An Observational, retrospective, and non-randomized study with patients enrolled in our telemonitoring program (May 2014-February 2018). We collected patients clinical data, telemonitoring transmissions, and HF decompensations.
Results:
A total of 240 patients were enrolled with a follow-up of 13.44 ± 8.65 months. During this interval, 527 HF decompensations in 148 different patients were detected. Significant weight increases, desaturation below 90% and perception of clinical worsening are good predictors of HF decompensation. We have built a predictive model applying machine learning (ML) techniques, obtaining the best results with the combination of “Weight + Ankle + well-being plus alerts of systolic and diastolic blood pressure, oxygen saturation, and heart rate.”
Conclusions:
ML techniques are useful tools for the analysis of HF datasets and the creation of predictive models that improve the accuracy of the actual remote patient telemonitoring programs.
Keywords : Heart failure decompensations; Hospital admissions; Remote patient telemonitoring; Artificial intelligence; Machine learning; Predictive models.