Transformer-based prediction of hospital readmissions for diabetes patients

Resumen

Artificial intelligence is having a strong impact on healthcare services, improving their quality and efficiency. This paper proposes and evaluates a prediction system of hospital readmissions for diabetes patients. This system is based on a Transformer, a state-of-the-art deep learning architecture integrating different types of information and features in the same model. This architecture integrates several attention heads to model the contribution of each feature to the global prediction. The main target of this work is to provide a decision support tool to help manage hospital resources effectively. This system was developed and evaluated using the United States Health Facts Database, which includes information and features from 101,766 diabetes patients between 1999 and 2008. The experiments were conducted using a patient-wise cross-validation strategy, ensuring that the patients used to develop the system were not used in the final test. These experiments demonstrated the Transformer’s strong ability to combine different features, providing slightly better results compared to previous results reported on this dataset. These experiments allow us to report the prediction accuracy for multiple class numbers. Finally, this paper provides a detailed analysis of the relevance of each feature when predicting hospital readmissions.