Cooperative dual medical ontology representation learning for clinical assisted decision-making

Predicting clinical events and providing assisted decision-making using Electronic Health Records (EHRs) play a central role in personalized healthcare. Despite the promising performance achieved for diagnosis and procedure predictions, most of the existing predictive models regard different medical codes as the same type and generally ignore the dependence between diagnoses and procedures in patients' admission history. To address these issues, we propose an end-to-end cooperative dual medical ontology representation learning framework for clinical assisted decision-making.

The framework consists of two primary modules: (1) dual medical ontology representation learning to facilitate the learning of medical concepts and (2) task dependent multi-task prediction to capture the correlation between diagnoses and procedures in patients' admission history. We evaluate our method with EHRs from the MIMIC-III Clinical Database, covering 6321 patients and 16335 visits.

Experiments conducted on the MIMIC-III dataset show that the proposed model achieves the best performance, with a top-20 accuracy of 58.20% for diagnosis prediction and a top-20 accuracy of 75.85% for procedure prediction. In addition, a series of experimental analyses and case studies further illustrate the excellent performance of our model.

We propose an end-to-end cooperative dual medical ontology representation learning framework, which achieves superior performance on multi-task diagnosis and procedure predictions. The source code is available at https://github.com/mhxu1998/CoDMO.