Using cross-domain knowledge augmentation to explore comorbidity in electronic health records data

Research concentrating on specific diseases or employing single datasets, such as medical histories and thematic data, has garnered considerable attention. However, there has been limited investigation into comorbidities. Although some ad-hoc methods have been utilized in the medical field, there is a scarcity of systematic approaches to address this challenge. The task of expressing patient features using heterogeneous and cross-domain data presents considerable difficulties. Directly mapping this data into a matrix frequently results in issues such as high dimensionality, sparsity, redundancy, and noise. Additionally, given the critical role of supervisory information in medicine, acquiring accurate information is paramount. To address these issues, we propose an enhanced clustering method that capitalizes on cross-domain knowledge augmentation. This method can iteratively learn clustering outcomes and cross-domain knowledge. The cross-domain knowledge matrix produced by our approach can be interpreted as a measure of similarity between instances across domains. We validate our proposed model in real-world electronic health records (EHR) data, and achieve significant performance improvement compared to the baseline method, successfully completing the task of exploring comorbidity. Due to the privacy of EHR data, we also conduct extensive experiments on the publicly available datasets DBLP and UCI. The experimental results show that our algorithm is superior to the baseline algorithm and has strong generality.