Diagnosis Prediction over Patient Data using Hierarchical Medical Taxonomies

A variety of hierarchical domain taxonomies exist in the medical domain for describing medical concepts such as laboratory tests, medications, and procedures. The structural information contained within domain taxonomies contains rich semantic information pertaining to the described concepts and their relationships to each other. As AI models are successfully applied in many medical areas, it is only natural to explore integrating AI models with medical domain taxonomies. However, only a few, nascent attempts have been made. In this work, we investigate how the structure of hierarchical medical taxonomies can be used to improve the performance of a diagnosis prediction task. Specifically, we suggest a method titled TreeEmb to pre-initialize the node embeddings of a patient graph derived from electronic health records using information from the taxonomy. We expect this method to improve the performance of graph convolution network models over the enriched patient graph. We evaluate our method over a patient graph created from the MIMIC-IV electronic health record dataset enriched by initializing node embeddings using hierarchical medical taxonomies. We use type-specific domain knowledge from hierarchical medical taxonomies such as the ICD-9 procedures, ATC medication, and LOINC laboratory test taxonomies. Experimental results from a multi-label diagnosis prediction task over this graph demonstrate the efficacy of our approach.