Predicting heart failure prognosis using deep learning based on FT-transformer

Abstract

Although heart failure (HF) diagnosis and treatment techniques have advanced, more than 50% of HF patients are readmitted. Readmission worsens the life quality of patients due to economic and psychological burdens. Therefore, readmission prediction for patients is important to prevent unnecessary readmissions. We used a feature tokenizer transformer (FT-transformer) to predict readmission by embedding all features and analyzing via transformer encoder. Our experiment with 615 HF patients outperformed conventional machine learning models, achieving an area under the curve of 0.7434 within 28 days, 0.7063 within 3 months, and 0.7039 within 6 months. FT-transformer can potentially improve patient outcomes by enabling early interventions to prevent readmissions.