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Prognosis Analysis of Heart Failure Based on Recurrent Attention Model - 13/09/19

Doi : 10.1016/j.irbm.2019.08.002 
J. Gong, X. Bai, D.-a. Li , J. Zhao, X. Li

Corresponding author.
Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le vendredi 13 septembre 2019

Abstract

Objectives

Heart failure is a group of complex clinical syndromes that lead to ventricular filling or impaired ejection ability due to abnormal heart structure or function. Difficult treatment, poor prognosis and high mortality are the main characteristics of heart failure. According to admission data and past medical use, the 30-day mortality rate of patients with heart failure was obtained and the main characteristics affecting the 30-day mortality of patients with heart failure were determined.

Material and methods

Based on the data of April 2016 to July 2018 of Shanxi Acadeny of Medical Sciences, and we chose 4,682 information on heart failure patients, of which 539 died in the hospital by screening. We built a 30-day mortality prediction model for patients with heart failure. The model can fuse clinical data and text data through multiple kernel learning, and input the fused data into the recurrent attention model. It can not only predict the 30-day mortality of patients with heart failure, but also the influencing factors of prognosis of patients with heart failure were also obtained.

Results

The prediction accuracy of the recurrent attention network is obviously higher than that of other machine learning models, and the accuracy rate reaches 93.4%. The AUC value of the area under the ROC curve of the model reaches 87%, which is obviously higher than that of the traditional machine learning models such as decision tree, naive Bayesian and support vector machine. In addition, the model can also reach a conclusion that New York heart function classification, age, NT—ProBNP, LVEF, β-blockers, ventricular arrhythmia, high blood pressure, coronary heart disease (CHD) and bronchitis were independent risk factors for death. And patients with revascularization, ACEI/ARB drugs, β-blockers, spironolactone have a better prognosis than non-users. This provides an important reference for doctors to better treat and manage patients with heart failure.

Conclusion

Experiments show that the prognostic effect of the recurrent attention model is significantly higher than that of other traditional machine learning models. Because the model increases the attention mechanism, the important features affecting the prognostic results are obtained, which enables doctors to prescribe drugs according to the symptoms, take timely precautions and help patients to treat in time.

Le texte complet de cet article est disponible en PDF.

Graphical abstract

Le texte complet de cet article est disponible en PDF.

Highlights

Multiple kernel learning can achieve the fusion of heterogeneous data.
RAN can predict the mortality of patients with heart failure within 30 days.
Attention mechanism can derive the important lethal factors.
The prognosis analysis model achieves accuracy of 93.4%.

Le texte complet de cet article est disponible en PDF.

Keywords : Heart failure, Recurrent attention model, Prognosis


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