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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Deep Learning Prediction Model of Mortality Including Brain-type Natriuretic Peptide in Patients with Acute Decompensated Heart Failure [PDF]

Hirotaka Takizawa, Masatoshi Minamisawa, Hirohiko Motoki, Koichiro Kuwahara, and Masaki Yamaguchi

(Received June 12, 2024; Accepted July 30, 2024)

Keywords: heart failure, deep learning, prediction model, mortality, brain-type natriuretic peptide

Predicting mortality in patients with acute decompensated heart failure remains difficult for non-specialists. In addition, the influence of various heart failure complications on mortality has not been sufficiently confirmed. The purpose of this research is to assess the possibility of predicting the mortality risk in patients with acute decompensated heart failure after discharge using deep learning based on a registry of Japanese hospitalized patients with high rates of comorbid atrial fibrillation, chronic kidney disease, and anemia. We randomly divided data from fifteen clinical characteristics in 1,012 hospitalized patients into training and validation datasets. Next, we introduced the datasets into a prediction model using an automated-deep learning algorithm (Prediction One). Our deep learning-based model demonstrated a high ability to predict mortality risk (c-statistics = 0.75, sensitivity = 0.607, and 1 − specificity = 0.192). Prediction accuracy can be improved by appropriately incorporating input variables such as the brain-type natriuretic peptide level, red blood cell count, left ventricular ejection fraction, number of administered medications, length of hospitalization, and Nohria–Stevenson classification stage. We demonstrated that our deep learning model based on multiple clinical characteristics is useful for predicting the mortality risk in hospitalized patients with heart failure. In particular, we showed that our model including brain-type natriuretic peptide is effective for predicting the acute decompensated heart failure mortality risk.

Corresponding author: Masaki Yamaguchi




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