Predicting risk of hospital readmission for comorbidity patients through a novel deep learning framework

Abstract

Hospital readmission is widely recognized as indicator of inpatient quality of care which has significant impact on healthcare cost. Thus, early recognition of readmission risk has been of growing interest in various hospitals. Additionally, there has been growing attention to provide better care to patients with more complications, whose care would impact the quality of care in multiple directions. To this regard, this research specifically targets comorbidity patients i.e., the patients with chronic disease. This research proposes a novel deep learning-framework termed SDAE-GAN. The presented approach consists of three phases. Firstly, various groups of variables from heterogeneous sources are collated. These variables mainly include demographic, socioeconomic, some statistics about patient's frequent admissions and their diagnosis codes. Then, more processing applies dealing missing values, digitization and data balancing. Afterwards, stacked denoising auto-encoders function to learn underlying representation; and technically to forms a latent space. The latent variables then are used by a Generative Adversarial Neural Networks to evaluate the risk of 30-day readmission. The model is fine-tuned and being compared with state-of-the-arts. Experimental results exhibit competitive performance with higher sensitivity.